diff --git a/app/public/data/gtif/data/data_descriptors/ari.json b/app/public/data/gtif/data/data_descriptors/ari.json deleted file mode 100644 index 6a4c836211..0000000000 --- a/app/public/data/gtif/data/data_descriptors/ari.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "name": "Aggregated health risk index (ARI)", - "specs": { - "unit": 1, - "range": "0-10", - "temporal_resolution": "1D", - "temporal_coverage": "20191001-20230125", - "timeliness": "1D", - "spatial_resolution": "Austrian gemeinde level", - "spatial_coverage": "Alpine Austria" - } -} \ No newline at end of file diff --git a/app/public/data/gtif/data/data_descriptors/flux.json b/app/public/data/gtif/data/data_descriptors/flux.json deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/app/public/data/gtif/data/data_descriptors/no2_series.json b/app/public/data/gtif/data/data_descriptors/no2_series.json deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/app/public/data/gtif/data/data_descriptors/pm10.json b/app/public/data/gtif/data/data_descriptors/pm10.json deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/app/public/data/gtif/data/data_descriptors/pm25.json b/app/public/data/gtif/data/data_descriptors/pm25.json deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/app/public/data/gtif/data/dem_10m_correct.tif b/app/public/data/gtif/data/dem_10m_correct.tif deleted file mode 100644 index d759e58353..0000000000 Binary files a/app/public/data/gtif/data/dem_10m_correct.tif and /dev/null differ diff --git a/app/public/data/gtif/data/dict_lau_id.json b/app/public/data/gtif/data/dict_lau_id.json deleted file mode 100644 index d9890e1023..0000000000 --- a/app/public/data/gtif/data/dict_lau_id.json +++ /dev/null @@ -1,1957 +0,0 @@ -{ - 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} - }, - { - "id": "PVEPPMwhHP", - "type": "fill", - "source": "innsbruck", - "source-layer": "wien_4esa", - "filter": ["all"], - "layout": {"visibility": "visible"}, - "paint": { - "fill-color": [ - "interpolate", - ["linear"], - ["get", "PVEPPMwhHP"], - 0, - ["to-color", "#f6f84c"], - 10, - ["to-color", "#ffc843"], - 15, - ["to-color", "#ff9d49"], - 20, - ["to-color", "#f67758"], - 25, - ["to-color", "#df556c"], - 35, - ["to-color", "#c33684"], - 75, - ["to-color", "#9f1b9b"], - 200, - ["to-color", "#400f9c"], - 500, - ["to-color", "#400f9c"], - 1400, - ["to-color", "#000c83"] - ] - } - }, - { - "id": "PVEPPMwhRP", - "type": "fill", - "source": "innsbruck", - "source-layer": "wien_4esa", - "filter": ["all"], - "layout": {"visibility": "visible"}, - "paint": { - "fill-color": [ - "interpolate", - ["linear"], - ["get", "PVEPPMwhRP"], - 0, - ["to-color", "#f6f84c"], - 10, - ["to-color", "#ffc843"], - 15, - ["to-color", "#ff9d49"], - 20, - ["to-color", "#f67758"], - 25, - ["to-color", "#df556c"], - 35, - ["to-color", "#c33684"], - 75, - ["to-color", "#9f1b9b"], - 200, - ["to-color", "#400f9c"], - 500, - ["to-color", "#400f9c"], - 1400, - ["to-color", "#000c83"] - ] - } - }, - { - "id": "PVEPPMwhLP", - "type": "fill", - "source": "innsbruck", - "source-layer": "wien_4esa", - "filter": ["all"], - "layout": {"visibility": "visible"}, - "paint": { - "fill-color": [ - "interpolate", - ["linear"], - ["get", "PVEPPMwhLP"], - 0, - ["to-color", "#f6f84c"], - 10, - ["to-color", "#ffc843"], - 15, - ["to-color", "#ff9d49"], - 20, - ["to-color", "#f67758"], - 25, - ["to-color", "#df556c"], - 35, - ["to-color", "#c33684"], - 75, - ["to-color", "#9f1b9b"], - 200, - ["to-color", "#400f9c"], - 500, - ["to-color", "#400f9c"], - 1400, - ["to-color", "#000c83"] - ] - } - }, - { - "id": "GRImpScore", - "type": "fill", - "source": "innsbruck", - "source-layer": "wien_4esa", - "filter": ["all"], - "layout": {"visibility": "visible"}, - "paint": { - "fill-color": [ - "interpolate", - ["linear"], - ["get", "GRImpScore"], - 0, - ["to-color", "#027224"], - 57.10, - ["to-color", "#a5ce40"], - 71.75, - ["to-color", "#dcee49"], - 78.95, - ["to-color", "#fceb49"], - 83.41, - ["to-color", "#ffc640"], - 86.94, - ["to-color", "#ff9f32"], - 91.33, - ["to-color", "#ff742c"], - 100, - ["to-color", "#ff3624"] - ] - } - } - ] -} \ No newline at end of file diff --git a/app/public/data/gtif/data/vienna_landcover_mercator.tif b/app/public/data/gtif/data/vienna_landcover_mercator.tif deleted file mode 100644 index af4b2ae1a4..0000000000 Binary files a/app/public/data/gtif/data/vienna_landcover_mercator.tif and /dev/null differ diff --git a/app/public/data/gtif/images/GR_70101030_chart.png b/app/public/data/gtif/images/GR_70101030_chart.png deleted file mode 100644 index a0f0a8fe95..0000000000 Binary files a/app/public/data/gtif/images/GR_70101030_chart.png and /dev/null differ diff --git a/app/public/data/gtif/images/GR_70101420_chart.png b/app/public/data/gtif/images/GR_70101420_chart.png deleted file mode 100644 index 0f5e071ffa..0000000000 Binary files a/app/public/data/gtif/images/GR_70101420_chart.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/FCM_dist_type.png b/app/public/data/gtif/images/legends/FCM_dist_type.png deleted file mode 100644 index 38b7930db0..0000000000 Binary files a/app/public/data/gtif/images/legends/FCM_dist_type.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/FCM_forest_mask.png b/app/public/data/gtif/images/legends/FCM_forest_mask.png deleted file mode 100644 index 1c1790dbfe..0000000000 Binary files a/app/public/data/gtif/images/legends/FCM_forest_mask.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/REP1.png b/app/public/data/gtif/images/legends/REP1.png deleted file mode 100644 index 30254c03f4..0000000000 Binary files a/app/public/data/gtif/images/legends/REP1.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/REP2.png b/app/public/data/gtif/images/legends/REP2.png deleted file mode 100644 index 601ab72c30..0000000000 Binary files a/app/public/data/gtif/images/legends/REP2.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/SOL_TEP.png b/app/public/data/gtif/images/legends/SOL_TEP.png deleted file mode 100644 index 02a0d8f760..0000000000 Binary files a/app/public/data/gtif/images/legends/SOL_TEP.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/SOL_temp.png b/app/public/data/gtif/images/legends/SOL_temp.png deleted file mode 100644 index b5c32e4766..0000000000 Binary files a/app/public/data/gtif/images/legends/SOL_temp.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/SPEI.png b/app/public/data/gtif/images/legends/SPEI.png deleted file mode 100644 index 248eb0ebab..0000000000 Binary files a/app/public/data/gtif/images/legends/SPEI.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/air_quality_ihr.png b/app/public/data/gtif/images/legends/air_quality_ihr.png deleted file mode 100644 index 88edc4bec3..0000000000 Binary files a/app/public/data/gtif/images/legends/air_quality_ihr.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/air_quality_pm.png b/app/public/data/gtif/images/legends/air_quality_pm.png deleted file mode 100644 index afe97a551a..0000000000 Binary files a/app/public/data/gtif/images/legends/air_quality_pm.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/gr_existing.png b/app/public/data/gtif/images/legends/gr_existing.png deleted file mode 100644 index c8242cdf2a..0000000000 Binary files a/app/public/data/gtif/images/legends/gr_existing.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/gr_impact.png b/app/public/data/gtif/images/legends/gr_impact.png deleted file mode 100644 index 8ed08c8592..0000000000 Binary files a/app/public/data/gtif/images/legends/gr_impact.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/gr_potential.png b/app/public/data/gtif/images/legends/gr_potential.png deleted file mode 100644 index 7c45f2c73b..0000000000 Binary files a/app/public/data/gtif/images/legends/gr_potential.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/pv_existing.png b/app/public/data/gtif/images/legends/pv_existing.png deleted file mode 100644 index 267d6fa875..0000000000 Binary files a/app/public/data/gtif/images/legends/pv_existing.png and /dev/null differ diff --git a/app/public/data/gtif/images/legends/worldcover.png b/app/public/data/gtif/images/legends/worldcover.png deleted file mode 100644 index 51ba60f00d..0000000000 Binary files a/app/public/data/gtif/images/legends/worldcover.png and /dev/null differ diff --git a/app/public/data/gtif/markdown/ADO.md b/app/public/data/gtif/markdown/ADO.md deleted file mode 100644 index fdb81b00f8..0000000000 --- a/app/public/data/gtif/markdown/ADO.md +++ /dev/null @@ -1,9 +0,0 @@ -Data from https://ado.eurac.edu/ - -
- -
- -
- -
\ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ.md b/app/public/data/gtif/markdown/AQ.md deleted file mode 100644 index 2931d34a11..0000000000 --- a/app/public/data/gtif/markdown/AQ.md +++ /dev/null @@ -1,5 +0,0 @@ -
- geosphere - Mindearth Logo - SISTEMA Logo -
diff --git a/app/public/data/gtif/markdown/AQ1.md b/app/public/data/gtif/markdown/AQ1.md deleted file mode 100644 index ca978070fe..0000000000 --- a/app/public/data/gtif/markdown/AQ1.md +++ /dev/null @@ -1,16 +0,0 @@ -Click on a bin on the map to display a scatterplot of your currently selected parameter against Sentinel5-p measurements. - -### Disclaimer -This tool allows exploring potential correlations between Sentinel-5P/TROPOMI measurements and human mobility data, with the objective to better understand how people's movement and mobility affects air quality parameters. - -For this purpose, both data sources were aggregated to the same spatial grid. For the air quality parameters daily S-5P observations were used. For the mobility data, only observations that preceded the S-5P observation by four hours was considered. Four hours is the typical time that NO2 can remain in the atmosphere and thus be detected. - -The mobility data only captures around 22% of the population and is then calibrated with population presence and density. Thus, it is one form of capturing people’s movement but likely not the most suitable. Consequently, estimates provided for Number of Trajectories, Congestion Index, Speed, Motorized Count, and Motorized Share may not be reflecting the reality of mobility on the ground with very high precision. - -Being a demonstrator on how to integrate and interactively compare air quality parameters versus mobility statistics, further validation is needed to be able to allow meaningful conclusions. - -
- geosphere - Mindearth Logo - SISTEMA Logo -
diff --git a/app/public/data/gtif/markdown/AQ1_congestion_index.md b/app/public/data/gtif/markdown/AQ1_congestion_index.md deleted file mode 100644 index 70a1c7fd65..0000000000 --- a/app/public/data/gtif/markdown/AQ1_congestion_index.md +++ /dev/null @@ -1 +0,0 @@ -Congestion index \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ1_mean_value.md b/app/public/data/gtif/markdown/AQ1_mean_value.md deleted file mode 100644 index 4464af1fdf..0000000000 --- a/app/public/data/gtif/markdown/AQ1_mean_value.md +++ /dev/null @@ -1 +0,0 @@ -Mean value \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ1_motorized_count.md b/app/public/data/gtif/markdown/AQ1_motorized_count.md deleted file mode 100644 index ca7eeb061f..0000000000 --- a/app/public/data/gtif/markdown/AQ1_motorized_count.md +++ /dev/null @@ -1 +0,0 @@ -Motorized count \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ1_motorized_share.md b/app/public/data/gtif/markdown/AQ1_motorized_share.md deleted file mode 100644 index 0719e565b3..0000000000 --- a/app/public/data/gtif/markdown/AQ1_motorized_share.md +++ /dev/null @@ -1 +0,0 @@ -Motorized trip share index \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ1_satellite_values.md b/app/public/data/gtif/markdown/AQ1_satellite_values.md deleted file mode 100644 index a2f987441a..0000000000 --- a/app/public/data/gtif/markdown/AQ1_satellite_values.md +++ /dev/null @@ -1 +0,0 @@ -Sentinel5-p NO2 \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ1_speed.md b/app/public/data/gtif/markdown/AQ1_speed.md deleted file mode 100644 index e88b58b27d..0000000000 --- a/app/public/data/gtif/markdown/AQ1_speed.md +++ /dev/null @@ -1 +0,0 @@ -Traffic-free trip speed \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ1_trajectories.md b/app/public/data/gtif/markdown/AQ1_trajectories.md deleted file mode 100644 index ac54f3880e..0000000000 --- a/app/public/data/gtif/markdown/AQ1_trajectories.md +++ /dev/null @@ -1 +0,0 @@ -Trajectories count \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ4.md b/app/public/data/gtif/markdown/AQ4.md deleted file mode 100644 index 4795e64992..0000000000 --- a/app/public/data/gtif/markdown/AQ4.md +++ /dev/null @@ -1,24 +0,0 @@ -## Disclaimer - -Please note that the current indicators on Human Mobility displayed on the platform have not -been yet validated or calibrated to match the real-world scenario. The estimates presented are -intended as placeholders to provide an overview of what the final platform will offer. It is -essential to keep in mind that the estimates are based on data obtained from mobile devices -and, therefore, are susceptible to biases and limitations. For instance, not everyone owns a -mobile device with location-tracking capabilities, which can impact the accuracy and -representativeness of the data. Furthermore, location-based data only cover a small percentage -of the population, which requires the implementation of statistical aggregations and averaging -techniques. As a result, the detection of extreme events (e.g., severe traffic congestion), may be -limited in the output of the analyses. - -In summary, while the population presence and human mobility predictions derived from -location-based data can provide valuable insights into human behaviour, they should be used -with caution when drawing conclusions about people's mobility patterns. Specifically, predictions -may not always be accurate, and as such, should be interpreted as an approximation rather -than an exact representation of reality. Anyhow, despite their limitations, population presence -and human mobility predictions can still provide valuable insights into trends and patterns that -can support a comprehensive understanding of human behaviour. - -
- Mindearth Logo -
diff --git a/app/public/data/gtif/markdown/AQ4_congestion_index.md b/app/public/data/gtif/markdown/AQ4_congestion_index.md deleted file mode 100644 index ca7c7386af..0000000000 --- a/app/public/data/gtif/markdown/AQ4_congestion_index.md +++ /dev/null @@ -1,13 +0,0 @@ -### Human Mobility Patterns - -#### Congestion index - -**Brief description:** The Congestion index represents the per cent decrease of the hourly average speed observed on the street with respect to the median speed observed during the trimester on that road which is considered the reference speed in normal conditions. Large values (i.e., a congestion index larger than ~30) highlight street congestion (that is, trips feature a lower average speed with respect to the ideal case) while smaller values indicate an ideal travel situation. - -**Unit:** percentage - -**Value Range:** 0 - 100 [%]; - -**Color Range:** linear scale [0 - 100] - -Congestion index diff --git a/app/public/data/gtif/markdown/AQ4_distance.md b/app/public/data/gtif/markdown/AQ4_distance.md deleted file mode 100644 index bfb35620db..0000000000 --- a/app/public/data/gtif/markdown/AQ4_distance.md +++ /dev/null @@ -1,13 +0,0 @@ -### Human Mobility Patterns - -#### Trip distance - -**Brief description:** The Trip distance displays the average total trip distance (in km) of all the trajectories that pass on a street segment during a specific hour. Higher values correspond to a significant amount of long trips passing on the street edge, whereas lower values highlight a predominance of short trips passing on the segment. - -**Unit:** km - -**Value Range:** 0 - 300 [km]; - -**Color Range:** linear scale [0, 300] - -Trip distance diff --git a/app/public/data/gtif/markdown/AQ4_duration.md b/app/public/data/gtif/markdown/AQ4_duration.md deleted file mode 100644 index 717493aeb7..0000000000 --- a/app/public/data/gtif/markdown/AQ4_duration.md +++ /dev/null @@ -1,14 +0,0 @@ -### Human Mobility Patterns - -#### Traffic-free trip duration - -**Brief description:** The Traffic-free trip duration represents the mean travel time (in minutes) that it takes to go from origin to destination for all the trips passing on a street segment during a specific hour in a hypothetical, traffic-free scenario. - -**Unit:** minutes - -**Value Range:** 0 - 60 [minutes]; - -**Color Range:** linear scale [0, 60] - -Traffic-free trip duration - diff --git a/app/public/data/gtif/markdown/AQ4_motorized_share.md b/app/public/data/gtif/markdown/AQ4_motorized_share.md deleted file mode 100644 index 140cda97df..0000000000 --- a/app/public/data/gtif/markdown/AQ4_motorized_share.md +++ /dev/null @@ -1,13 +0,0 @@ -### Human Mobility Patterns - -#### Motorized trip share index - -**Brief description:** The Motorized trip share index displays the daily percentage of trajectories traversing a street segment that is deemed to be carried out using motorized transportation (e.g., buses, cars, motorcycles, etc.) instead of non-motorized modes (mainly by bike or foot). The classification is based on speed, duration, distance, average speed, and other spatiotemporal features. - -**Unit:** percentage - **daily temporal resolution and not on hourly basis** - -**Value Range:** 0 - 100 [%]; - -**Color Range:** linear scale [0 - 100] - -Motorized trip share index diff --git a/app/public/data/gtif/markdown/AQ4_speed.md b/app/public/data/gtif/markdown/AQ4_speed.md deleted file mode 100644 index 976b7bd607..0000000000 --- a/app/public/data/gtif/markdown/AQ4_speed.md +++ /dev/null @@ -1,13 +0,0 @@ -### Human Mobility Patterns - -#### Traffic-free trip speed - -**Brief description:** The Traffic-free trip speed reports the average speed [km/h] of all trajectories passing on a street segment during a specific hour in the traffic-free scenario. - -**Unit:** km/h - -**Value Range:** 0 - 130 [km/h]; - -**Color Range:** linear scale [0 - 130] - -Traffic-free trip speed diff --git a/app/public/data/gtif/markdown/AQ4_trajectories.md b/app/public/data/gtif/markdown/AQ4_trajectories.md deleted file mode 100644 index 96218f3c63..0000000000 --- a/app/public/data/gtif/markdown/AQ4_trajectories.md +++ /dev/null @@ -1,13 +0,0 @@ -### Human Mobility Patterns - -#### Trajectories count - -**Brief description:** The Trajectories count displays the total number of trajectories traversing a street segment during a specific hour. A trajectory is defined as a trip that a person undergoes between two consecutive stops during his daily commuting. - -**Unit:** #trajectories - -**Value Range:** 0 - 4 000 [#trajectories] - -**Color Range:** logarithmic scale [1 - 4000] - -Trajectories count \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ5.md b/app/public/data/gtif/markdown/AQ5.md deleted file mode 100644 index 5669547424..0000000000 --- a/app/public/data/gtif/markdown/AQ5.md +++ /dev/null @@ -1,9 +0,0 @@ -One of the variables which is linked to pollutant sectors such as traffic and industrial -activities is the tropospheric nitrogen dioxide (NO2). This data are collected daily -worldwide by the Copernicus Sentinel 5P satellite. - -

- Sistema Logo - Mindearth Logo - GeoSphere Logo -

diff --git a/app/public/data/gtif/markdown/AQ_IHR.md b/app/public/data/gtif/markdown/AQ_IHR.md deleted file mode 100644 index 3b7bef4243..0000000000 --- a/app/public/data/gtif/markdown/AQ_IHR.md +++ /dev/null @@ -1,46 +0,0 @@ -
- -## Aggregate Risk Index - -The Aggregate Risk Index (ARI) is an index that provides a measure of the health outcome-specific risk due to the short-term exposure to multiple air pollutants (PM10, PM2.5, NO2, SO2 and O3). It is derived by additively summing the product of pollutant concentration weighted -using health outcome-specific Relative Risk factors of increased mortality due to all causes, provided by the World Health Organization. The values displayed on the platform represent the geographical mean values of the daily average for each Local Administrative Unit (LAU) - -More info about the ARI index can be found here: - - * [What are the WHO Air quality guidelines?](https://www.who.int/news-room/feature-stories/detail/what-are-the-who-air-quality-guidelines) - * [Health Advice ARI | AQ map (dlr.de)](https://www.wdc.dlr.de/AlpAirEO/healthadvARI) - -
-
-
- - - - - - - - - - - - - - - - - - - - - - - -
- Increased health risk from current air pollution and health advice for the general population (age classes 15 to 64, without pre-existing conditions) -
ARIIncreased health risk from current air pollutionHealth advice and preventive measures
0lowIdeal conditions for outdoor activities.
1
2
3
4moderateNo need to modify your usual outdoor activities unless you experience symptoms.
5
6
7highAnyone experiencing discomfort such as coughing or throat irritation should consider reducing or rescheduling strenuous outdoor activities to periods when the index is lower.
8
9
10very highHealth alert! -

Everyone may experience more serious health effects.

-

Everyone should reduce or reschedule physical exertion, particularly outdoor activities to times when the index is lower.

-

If you feel uncomfortable, contact your doctor or general practitioner.

- -
\ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ_PM10.md b/app/public/data/gtif/markdown/AQ_PM10.md deleted file mode 100644 index ba0d79a4d6..0000000000 --- a/app/public/data/gtif/markdown/AQ_PM10.md +++ /dev/null @@ -1,5 +0,0 @@ -
- -## Particulate Matter 10µm - -Particulate Matter with diameters less than 10µm - mass concentration in µg/m³ obtained daily from the [Copernicus Atmosphere Monitoring Service (CAMS)](https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts?tab=overview) European air quality forecasts dataset.The value displayed on the platform represents the geographical mean and maxima values of the daily average pollutant concentration for each Local Administrative Unit (LAU) \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AQ_PM25.md b/app/public/data/gtif/markdown/AQ_PM25.md deleted file mode 100644 index 661e3fcc30..0000000000 --- a/app/public/data/gtif/markdown/AQ_PM25.md +++ /dev/null @@ -1,5 +0,0 @@ -
- -## Particulate matter 2.5µm - -Particulate Matter with diameters less than 2.5µm - mass concentration in µg/m³ obtained daily from the [Copernicus Atmosphere Monitoring Service (CAMS)](https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts?tab=overview) European air quality forecasts dataset.The value displayed on the platform represents the geographical mean and maxima values of the daily average pollutant concentration for each Local Administrative Unit (LAU) \ No newline at end of file diff --git a/app/public/data/gtif/markdown/AT_AQ2.md b/app/public/data/gtif/markdown/AT_AQ2.md deleted file mode 100644 index 15a5d0f7d6..0000000000 --- a/app/public/data/gtif/markdown/AT_AQ2.md +++ /dev/null @@ -1,11 +0,0 @@ -## NO2 concentrations measured at IAO - -NO2 has primarily produced by car traffic and has emerged as a primary public health concern in many regions, including the Alps. -Regulatory thresholds of NO2 are violated at many roadside stations along the Inn Valley despite a decreasing general trend due to -regulatory actions promoted by EU. - -
- Luftblick Logo - University Innsbruck Logo - DLR Logo -
diff --git a/app/public/data/gtif/markdown/BM2.md b/app/public/data/gtif/markdown/BM2.md deleted file mode 100644 index 5feaac1445..0000000000 --- a/app/public/data/gtif/markdown/BM2.md +++ /dev/null @@ -1,13 +0,0 @@ -## Biomass - - - -Humans have exploited forest biomass as a material and energy source for millennia, but population growth and increasing demand for resources have diminished the extent and condition of forests, including the amount of carbon they store and exchange with the atmosphere. - -The [Global Climate Observing System](https://gcos.wmo.int/) considers above-ground biomass (AGB) an Essential Climate Variable due to its functions as both a source of atmospheric CO2 (and other greenhouse gases) when forest is lost under Land Use Change or by degradation, and as a sink for CO2 due to forest growth. - -Information on forest biomass can also play a much wider role in understanding and predicting climate, for example in model initialisation, model testing, estimation of carbon turnover, inferring the forest disturbance regime, and data assimilation in carbon cycle and climate models. - -The Above Ground Biomass map over Austria is extracted from the European-wide biomass map 2021 from the Forest Carbon Monitoring project. The products rely on ESA's C-band (Sentinel 1A) and JAXA's L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) annual mosaic, with additional information from space borne LIDAR (e.g. NASA's Global Ecosystem Dynamics Investigation Lidar GEDI). The combination of these sensor types allows to retrieve information on the amounts of foliage and woody plant material and their distribution, both spatially and vertically, and over time. - -[![Forest Carbon Monitoring Logo](data/gtif/images/logos/forest.png "Forest Carbon Monitoring Logo")](https://www.forestcarbonplatform.org/) \ No newline at end of file diff --git a/app/public/data/gtif/markdown/E12c.md b/app/public/data/gtif/markdown/E12c.md deleted file mode 100644 index 0163c8678c..0000000000 --- a/app/public/data/gtif/markdown/E12c.md +++ /dev/null @@ -1,62 +0,0 @@ -## Mobility: Sentinel-2 based truck detections - -The truck detection indicator was developed within the context of the [RACE](https://race.esa.int/) (Rapid -Action for Citizens with EO)] dashboard initiative, initially developed to ascertain an -impact of the COVID19 pandemic on truck based logistic routes in Europe. The -indicator exploits the parallax effect created by large moving targets in Sentinel-2 -(S2) muti-spectral imagery (see details below). The initial indicator was proposed in -the context of a competition. Later this algorithm was transformed into a on-demand -process that can be triggered within the [EuroDataCube](https://eurodatacube.com/) marketplace. -In the context of GTIF this indicators is also of relevance especially in the mobility -transition and how transport and the main logistic corridors, such as the Brenner -Pass in Austria, might correlate with air quality indicators derived from Sentinel-5P -TROPOMI and other satellites as well as modelling results. -The in GTIF integrated truck detection results represent monthly detection heatmaps -(produced for motorways and major roads, separately) as well as the individual point -detections. Users can draw an area of interest on the heatmap layers to produce -time series charts, and the individual detections are visualised along with the -corresponding S2 image. - - -## RACE indicator: Regional motorway truck traffic - -Prepared by Henrik Fisser within the [upscaling part](https://eo4society.esa.int/2020/04/24/from-the-covid-19-custom-script-contest-to-the-euro-data-cube-european-dashboard/) of the [EuroDataCube](https://eurodatacube.com/) [COVID-19 -Custom Script Contest](https://www.sentinel-hub.com/contest-covid/).This indicator is based on the detection of moving trucks on -motorways in the EU. The detection uses data from the [Copernicus Sentinel-2](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2) satellite. - -___________ -### Truck traffic as an economic indicator -Transportation on roads is by far the [most important mode of cargo logistics](https://ec.europa.eu/eurostat/statistics-explained/index.php/Freight_transport_statistics_-_modal_split) in Europe. The amount of roaming trucks may indicate how the economy of a country is performing. Germany for instance uses truck counts from toll statistics as a [short-term economic indicator](https://www.destatis.de/EN/Service/EXDAT/Datensaetze/truck-toll-mileage.html). Detecting trucks with satellite imagery has traditionally not been attempted at relatively coarse resolution, but a [new detection approach](https://www.sentinel-hub.com/contest/#ResultsCovid) now facilitates extracting moving trucks from Sentinel-2 imagery. - -The truck indicator shows if there has been an unnormal low or high number of trucks on primary roads and trucks within the respective region of interest. - -### Coverage - -Truck detections are performed over non-cloudy areas, using all the available Sentinel-2 acquisitions over the EU area between January 2020 and December 2021. - -### Sentinel-2 truck detection -This indicator is based on a method that enables to detect trucks on a large scale using Sentinel-2 data. The method exploits an effect related to the [Sentinel-2 Multispectral Instrument (MSI)](https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument) geometry. Sentinel-2 does not see a moving truck once but three times in the red-blue-green wavelengths. As the truck keeps traveling during this short time offset, it appears spectrally disassembled. This pattern may be used for detecting roaming trucks on roads. Although visual inspection cannot confirm that the objects are trucks, this is implied by the ratio between size of different vehicles and a Sentinel-2 pixel (see validation for details). However, a confusion with moving vehicles of similar size such as buses may occur. -In order to generally reduce false detections the computation is constrained to road data from [Open Street Maps](https://wiki.openstreetmap.org/wiki/Key:highway) (OSM). The blue dots in Fig. 1 are examples of identified trucks in these subsets. - -![](./eodash-data/stories/E12c_2018-04-19_france.jpg) - -*Fig. 1: Truck detection example in southern France* - -## The impacts of COVID-19 on motorway truck traffic - -Detected trucks in 2020 were compared with the median value of the baseline years 2017, 2018 and 2019. In each period the number of detected trucks in an area is divided by the number of observations in order to make the figures comparable across the continent. The counts of each year are compared with the baseline value, which is the median of the three baseline years. The median is used in order to reduce arbitrary oscillations of the baseline. Truck counts higher or lower 5 % than the baseline are denoted as 'High' or 'Low' respectively. - -### Overall results -In average, 15 % less trucks than normal were observed on motorways in the EU during the considered period. However, the figures vary across the EU as depicted in Fig. 2. The map might already suggest some patterns, nevertheless it is worth looking into the details. Lockdown measures have been diverse across the EU both in duration and intensity. Depending on area-specific data availability and the lockdown periods effects directly induced by the measures as well as potential catch-up effects might be captured. - -![](./eodash-data/stories/E12c_motorway_map.png) - -*Fig. 2: Trucks on motorways - indicator results in EU regions* - -### Validation -The Sentinel-2 truck detection method has been validated with [German traffic count stations](https://www.bast.de/BASt_2017/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/zaehl_node.html) on the two motorways A3 and A61. This validation implies that the Sentinel-2 method detects in average 78 % of the trucks counted at the four used stations (Fig. 3), though with some variety. It also suggests that detected objects are mostly trucks and not cars as the number of counted cars at the count stations is approximately three to four times higher than the number of trucks. -Since the station data is hourly, its mean per 15 minutes was calculated for the date and time of the respective Sentinel-2 acquisition. It has to be kept in mind that the validation is yet limited to Germany and that an area-specific uncertainty is introduced by potential haze during large-scale processing. - -![](./eodash-data/stories/E12c_validation_percentages.png) - -*Fig. 3: Trucks detected by Sentinel-2 vs. station counts [%]* diff --git a/app/public/data/gtif/markdown/EO4A.md b/app/public/data/gtif/markdown/EO4A.md deleted file mode 100644 index 97248b45bc..0000000000 --- a/app/public/data/gtif/markdown/EO4A.md +++ /dev/null @@ -1,9 +0,0 @@ -## EO4Alps-Snow - -#### Earth Observation for operational hydrology - Alps regional initiative - -The [eo4alps project](https://snow-app-gte2s.hub.eox.at/) focuses on implementing a high-resolution quasi real-time snow monitoring to improve water resource management. - -It is taking advantage of the recent developments in physically-based snow modelling and is based on high-resolution optical and radar EO missions such as Sentinel-1 and 2. - -The core service is a snow water equivalent (SWE) product generated using a cloud based processing environment to be delivered over the entire Alpine arc region. The eo4alps team is planning to engage users from public and private sectors, such as public agencies, research centers, associations and hydropower companies. \ No newline at end of file diff --git a/app/public/data/gtif/markdown/FCM1.md b/app/public/data/gtif/markdown/FCM1.md deleted file mode 100644 index b2be8a39e7..0000000000 --- a/app/public/data/gtif/markdown/FCM1.md +++ /dev/null @@ -1,5 +0,0 @@ - -### Disclaimer - -The forest change detections is based on a limited amount of training data, which is unlikely to represent all possible phenological conditions or forest structure types. -This prototype is not validated and should therefore be considered as an example of the type of product which could be created based on the implemented methodology and training data available so far. \ No newline at end of file diff --git a/app/public/data/gtif/markdown/FCM2.md b/app/public/data/gtif/markdown/FCM2.md deleted file mode 100644 index 3168ab59d5..0000000000 --- a/app/public/data/gtif/markdown/FCM2.md +++ /dev/null @@ -1,8 +0,0 @@ - - -
- -### Disclaimer - -The forest disturbance type classification output is based on a limited amount of training data, which is unlikely to represent all possible phenological conditions or forest structure types. -This prototype is not validated and should therefore be considered as an example of the type of product which could be created based on the implemented methodology and training data available so far. \ No newline at end of file diff --git a/app/public/data/gtif/markdown/FCM3.md b/app/public/data/gtif/markdown/FCM3.md deleted file mode 100644 index 6db06e049f..0000000000 --- a/app/public/data/gtif/markdown/FCM3.md +++ /dev/null @@ -1,7 +0,0 @@ - -
- -### Disclaimer - -The forest mask classification output is based on a limited amount of training data, which is unlikely to represent all possible phenological conditions or forest structure types. -This prototype is not validated and should therefore be considered as an example of the type of product which could be created based on the implemented methodology and training data available so far. \ No newline at end of file diff --git a/app/public/data/gtif/markdown/LST.md b/app/public/data/gtif/markdown/LST.md deleted file mode 100644 index 9015b8a16a..0000000000 --- a/app/public/data/gtif/markdown/LST.md +++ /dev/null @@ -1,24 +0,0 @@ -## Heat Stress over Austria - -The data viewer provides a uniform coverage of observations of the ECOSTRESS instrument mounted on the International Space Station. - -Data shows land surface temperatures measured during early afternoon hours in °C. The map is composed of single observations acquired between June 1st to June 24th, in order to result in an almost cloud-free image. - -Additional analytical elements are presented on commune level featuring min, max and mean land surface temperatures, as well as the percentage of the residential population exposed to surface temperatures above 35°C. - -Provided by -[![GeoVille Logo](data/gtif/images/logos/geoville.png "GeoVille Logo")](https://www.geoville.com) -Original version available at [https://heatmap.geoville.com](https://heatmap.geoville.com) - -## Background - -June 2022 marked a record-breaking month with air temperatures in excess of the historic average for the time of year in Central Europe. Monthly average temperature was at 2.3°C above the long-term average for 1991-2020, and bigger Austrian cities like Vienna or Innsbruck featured more than 10 heat days with maximum temperatures exceeding 30°. - -![Monthly average temperatures for major Austrian cities (data source: ERA5 reanalysis data / Copernicus Climate Change Service; processed by GeoVille)](https://heatmap.geoville.com/heatmap.png "Monthly average temperatures for major Austrian cities (data source: ERA5 reanalysis data / Copernicus Climate Change Service; processed by GeoVille)") -*Figure: Monthly average temperatures for major Austrian cities (data source: ERA5 reanalysis data / Copernicus Climate Change Service; processed by GeoVille)* - -Air temperature is different to the temperature on the land surface and the subjective perceived temperature. Higher air temperatures, especially during longer heat waves, cause a strong increase of surface temperatures especially in areas which are built-up or sealed by concrete or asphalt. This is known as the Urban Heat Island effect, where an urban area is significantly warmer than its surrounding rural areas. Heat islands contribute to higher daytime temperatures, reduced nighttime cooling, and higher air-pollution levels, in turn amplifying heat-related health issues and stress factors on both humans and animals. Studies show that depending on meteorological factors, such as humidity or wind, subjective heat stress starts at around 23-25°, and intense heat stress is felt above 35°. - -Responding to global warming and adapting to climate change effects such as heat waves and drought is a key priority of Austria’s national Climate Change Adaptation Strategy. Cities are increasingly responding to this threat by formulating heat protection plans and initiating adaptation measures. Adaptation options include the reduction of heat emissions from fuel combustion or the climate proofing of buildings through better isolation. Nature-based solutions are increasingly followed, as these are longer-lasting with benefits for a variety of sectors. Such ‘green measures’ entail establishing a better land use mix by limiting the construction of new impermeable surfaces to reduce heat absorption, and offering shade and cooling through more green spaces and open water. - -Land surface temperature is one of 54 Essential Climate Variables defined by the Global Climate Observing System to monitor climate change. Satellite-based analyses of global land surface temperatures span several decades and are typically provided in moderate spatial resolution (up to 0.1°), such as from the Copernicus Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR). Landsat 9’s Thermal Infrared Sensor 2 (TIRS-2) records land surface temperature in two thermal infrared bands in 100m spatial resolution. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), an instrument owned by NASA’s Jet Propulsion Laboratory (JPL) carried on the International Space Station, measures land surface temperature in 70m spatial resolution and enables more localized assessments down to city scales. Through the European Copernicus program, the European Space Agency is planning to launch a Land Surface Temperature Monitoring (LSTM) mission (Sentinel-8) which will carry a thermal infrared sensor to provide global observations of land-surface temperature in so far unprecedented spatial resolution. diff --git a/app/public/data/gtif/markdown/MOBI.md b/app/public/data/gtif/markdown/MOBI.md deleted file mode 100644 index f3a8e2a2e2..0000000000 --- a/app/public/data/gtif/markdown/MOBI.md +++ /dev/null @@ -1,15 +0,0 @@ -## Disclaimer - -Please note that the provided estimates for Dynamic Human Presence may not be entirely -accurate, mainly due to the collection mechanisms of the data used to generate the estimates. -Indeed, location-based data collected from mobile devices can sometimes overestimate or -underestimate the number of people in a given area, depending on how many mobile devices -are being used and what applications are installed on those devices. This is because not -everyone has a GPS-enabled mobile device, and even those who do have mobile devices might -have different apps installed, which can affect the collected data. Generally, we expect the -population density and count estimates to have a relative error of approximately 10%, which -may increase in very densely or very sparsely populated areas. - -
- Mindearth Logo -
diff --git a/app/public/data/gtif/markdown/MOBI1_users_count.md b/app/public/data/gtif/markdown/MOBI1_users_count.md deleted file mode 100644 index 9a3aa9380a..0000000000 --- a/app/public/data/gtif/markdown/MOBI1_users_count.md +++ /dev/null @@ -1,14 +0,0 @@ -### Dynamic human presence - -#### Population count - -**Brief description:** The Population Count reports the number of people present in each Local -Administrative Unit (LAU) during a given hour. - -**Unit:** people in area - -**Value Range:** GEM level: [0, 100 000] - -**Color Range:** logarithmic scale [100, 100 000] - -Population count \ No newline at end of file diff --git a/app/public/data/gtif/markdown/MOBI1_users_density.md b/app/public/data/gtif/markdown/MOBI1_users_density.md deleted file mode 100644 index 666e4825f7..0000000000 --- a/app/public/data/gtif/markdown/MOBI1_users_density.md +++ /dev/null @@ -1,18 +0,0 @@ -### Dynamic human presence - -#### Population density - -**Brief description:** The Population Density [#people / km 2 ] reports the hourly density of people -per square kilometer within each Local Administrative Unit (LAU). A larger value denotes a -densely visited area whereas lower values correspond to more sparse (in terms of visitors) -areas. Population density allows us to effectively compare the occupation rate of census areas -featuring different surface extensions and amount of visitors. - -**Unit:** people/km2 - -**Value Range:** GEM level: [0, 10 000] - -**Color Range:** logarithmic scale [1, 10 000] - -Population density - diff --git a/app/public/data/gtif/markdown/REP1.md b/app/public/data/gtif/markdown/REP1.md deleted file mode 100644 index dccc21f82b..0000000000 --- a/app/public/data/gtif/markdown/REP1.md +++ /dev/null @@ -1,52 +0,0 @@ -
- -### Wind Power Density -The wind power density (w m 2 ) is a measure of the available wind resource. Higher wind power density -indicates greater wind power potential. - - -### Distance to Settlement -Each federal province in Austria has distance requirements between wind turbines and settlements. Increase the minimum distance to select areas further from settlements, you can chose between two distance layers: - -* WSF: the distance to settlements is based on the World Settlement Footprint (product obtained from satellite data) -* Austrian Cadaster: the distance to settlements layer is based on the national Austrian cadaster data - - -### Distance to transmission network -Wind turbines are optimally placed close to the existing transmission network. Reduce this distance to -select areas closer to the network. - - -### Protected areas -Conservation areas such as Natura 2000 sites and Nationally designated areas are protected, and should be -avoided when positioning wind turbines. - - -### Slope -Generally, flatter terrain is preferred for wind turbine locations. Typical suitability studies choose land with -slope of less than 15-20 degrees. - - -### Elevation -Generally, lower elevation areas are preferred for turbine locations. Selecting areas below 2,000 m avoids -many mountainous and inaccessible areas. - -### RIX -The Ruggedness Index is a measure of the steepness of the terrain in the surrounding area. Uncertainty in -wind density estimates increase with higher RIX values. - -
- -### World Cover classes - -Colors used in the trade-off explorer above. - -  - -  - -  - -  - -![DHI Logo](data/gtif/images/logos/dhi.png "DHI Logo") diff --git a/app/public/data/gtif/markdown/REP2.md b/app/public/data/gtif/markdown/REP2.md deleted file mode 100644 index 297b49f194..0000000000 --- a/app/public/data/gtif/markdown/REP2.md +++ /dev/null @@ -1,31 +0,0 @@ -
- -### Global horizontal irradiation (kWh/m²/day) - -The greater amount of solar irradiation, the more electricity can be generated by a solar cell module. -Selecting area with high solar potential contributes significantly to the desired efficiency and economic -feasibility of a solar power project. - -### Slope & Aspect - -Gentle terrain is preferred for solar panel installation. Typical suitability studies choose land with slope of -less than 5-15 degrees and with a slope orientation facing in south, southeast, southwest direction (i.e., an -aspect in the range of 135-225 degrees). - -### Elevation - -Installation of solar panels in high altitude areas (e.g., 1500 m amsl) can be challenging due to increased -transportation cost, suitability of technology, access to the grid, as well as routine and emergency -maintenance. - -### Distance to transmission network - -Solar power panels are optimally placed close to the existing transmission network. Reduce this distance to -select areas closer to the network. - -### Protected areas - -Solar power facilities are not typically proposed in conservation areas such as Natura 2000 sites and -nationally designated areas. - -![DHI Logo](data/gtif/images/logos/dhi.png "DHI Logo") diff --git a/app/public/data/gtif/markdown/REP3.md b/app/public/data/gtif/markdown/REP3.md deleted file mode 100644 index d8036567a7..0000000000 --- a/app/public/data/gtif/markdown/REP3.md +++ /dev/null @@ -1 +0,0 @@ -![DHI Logo](data/gtif/images/logos/dhi.png "DHI Logo") diff --git a/app/public/data/gtif/markdown/REP4.md b/app/public/data/gtif/markdown/REP4.md deleted file mode 100644 index decf5de9b6..0000000000 --- a/app/public/data/gtif/markdown/REP4.md +++ /dev/null @@ -1,5 +0,0 @@ -# HydroPower - -The hydro power (reservoir) module is used to present reservoir surface area, level and storage changes. First, we analyze individual reservoirs extracting water area from pre-processed optical and SAR satellite imageries using a machine learning algorithm. After outlier filtering and post-process, the timeseries graph are constructed for each individual reservoir. Where there is a cross-section with Sentinel 3A/B orbits the reservoir water levels are directly obtained from satellite altimetry. Where only SWE time series exist the storage level is estimated based on the nominal change in surface water area. - -![DHI Logo](data/gtif/images/logos/dhi.png "DHI Logo") diff --git a/app/public/data/gtif/markdown/REP5.md b/app/public/data/gtif/markdown/REP5.md deleted file mode 100644 index e737584046..0000000000 --- a/app/public/data/gtif/markdown/REP5.md +++ /dev/null @@ -1,42 +0,0 @@ -### Micro Hydropower - -**Product name:** Micro Hydropower - -**Coverage:** Austria (9.5°, 46.3°, 17.2°, 49.0°) - -**Product description:** - -Close to 300 locations for potential run-of-river hydroelectricity -generation plants were identified in Austria by utilizing geospatial techniques to analyze -hydrological data from Terra Climate and terrain data from Copernicus DEM. -The potential power generation capacity for the identified locations ranges from around 5 kW to around 5 mW. - -In the first step, flow direction and flow accumulation are calculated from the DEM, which -provides information on the direction of water flow and the amount of water that -accumulates in each cell. Next, streams are derived by selecting cells that have a flow -accumulation of between 5,000 and 1,000,000 upstream cells. This step allows for the -identification of suitable watercourses in the area. The streams are then split into 1 km -sections to allow for more detailed analysis of the flow patterns in each section of the -stream. - -As steep gradients are needed to create an adequate head for hydroelectricity generation, -potential pour points (center points of stream segments) are identified by selecting sections -with a slope above 50 m. The potential pour points are then snapped to the highest flow -accumulation cell within a maximum distance of 500 m to ensure that the pour points are in -areas where water flow is concentrated. - -To identify the areas that contribute to the flow of water at each pour point, watersheds are -derived from flow direction data. Subsequently, flow rates are calculated by taking zonal -statistics for each watershed area and runoff data from Terra Climate. Finally, hydropower -potential is calculated using flow rate, head on 1 km section, gravity and density of water. - -**Limitations:** - -The results shown here is a demonstrator of a rapid cost-effective investigation -method for identifying locations showing the greatest micro-hydro potential. However, it is a -first line assessment which require further investigation but it allow planners and decision -makers to quickly focus on areas showing the greatest potential for further investigation. - -**License:** Creative Commons CC BY-SA 3.0 IGO - -![DHI Logo](data/gtif/images/logos/dhi.png "DHI Logo") diff --git a/app/public/data/gtif/markdown/REP6.md b/app/public/data/gtif/markdown/REP6.md deleted file mode 100644 index 2d7d5d5198..0000000000 --- a/app/public/data/gtif/markdown/REP6.md +++ /dev/null @@ -1,89 +0,0 @@ -### Sentinel-2 Wind Turbine Detection - -**Product name:** Sentinel-2 Wind Turbine Detection - -**Spatial resolution:** Bounding Box - -**Temporal reference:** Autumn 2022 – Spring 2023 - -**CRS:** EPSG:4326 - WGS 84 - -**Value range:** - - -**Coverage:** Austria (9.5°, 46.3°, 17.2°, 49.0°) - -**Product description:** - -Wind turbine detections were made using Sentinel-2 imagery and a state-of-the-art object -detection neural network. - -The model was trained using wind turbine locations from Open Street Map as a starting -point. Data was gathered for Austria and the surrounding countries. This data was manually -cleaned by comparing it to high-resolution imagery and imagery from Sentinel-2. The OSM -data was found to be around 85% accurate before it was manually cleaned, and assumed -100% correct after this process. - -The model architecture used for bounding box prediction -was Faster R-CNN (https://arxiv.org/abs/1506.01497). Sentinel-2 RGB bands (10m) were -used as image input. - -Sentinel-2 imagery from the late Autumn, Winter, and early Spring were -used for model training and inference. This was chosen as wind turbines are most visible -during this period due to the long shadow cast by turbines. - -When evaluating early runs of the model, false detections of wind turbines were found in -mountainous regions and specific land cover types. For example, false detections in high -altitude areas and steep terrain where turbines are not typically found. Detecting wind -turbines in forested areas was particularly challenging, as the wind turbine’s shadow was not -visible in imagery. Similarly tall buildings and power lines were leading to false turbine -detections. Several filtering techniques were applied using Copernicus data sets and known -electric tower locations from Open Street Map. Detections with the following characteristics -were not considered valid and filtered out: - -1. Detections where the slope is great than 25 degrees (slope from Copernicus 10m DEM) -2. Detections where the elevation is great than 1850m (height from Copernicus 10m DEM) -3. Detections located in the following Corine Land Cover (2018) classes: -a. Discontinuous urban fabric -b. Port areas -c. Green urban areas -d. Sport and leisure facilities -e. Mineral extraction sites -4. Detections located within the Tree Cover class from ESA WorldCover (2021) -5. Detection that were located within 50m of electric towers from Open Street Map - -**Source URL:** -**Source DOI:** - - -© DHI A/S - / KEGR / 2023-05-26 - -**Input datasets:** - -**Main datasets:** Sentinel-2, Modified Wind Turbine locations from Open Street Map - -**Filtering datasets:** Copernicus DEM (10m), Corine Land Cover (2018), ESA WorldCover -(2021), Electric tower locations from Open Street Map - -**Reported accuracy:** - -The accuracy of this product was assessed in two steps: 1) to estimate the commission -error (Users accuracy) and 2) estimate of the omission error (Producers Accuracy). -To assess the commission error a simple random sample of 100 detected wind turbines -were chosen and visually validated. If the predicted bounding box was within 20m of a -visually confirmed wind turbine the detection was considered correct. -To assess the omission error a validation sample of 100 known locations of wind turbines -was compared to the detected bounding boxes. If the predicted bounding box was within -20m of a known wind turbine location the detection was considered correct. -The accuracy assessment showed the following: -Commission error: 13% - i.e. approx. 87% of the reported detections are TRUE locations of -wind turbines, while 13% are FALSE detections. -Omission error: 33% - i.e. approx. 67% of all wind turbines in Austria are detected by the -model, and 33% of turbines are not detected. -When we apply the same filtering for excluding urban/built-up areas and forested areas (see -Product description) the Omission error is reduced to 30%, meaning 70% of Austria’s wind -turbines in the remaining open land area are detected using Sentinel-2. - -**License:** Copyright: © 2023 DHI A/S. The data is published under a Creative Commons 4.0 -Attribution International license, CC BY 4.0 - - -![DHI Logo](data/gtif/images/logos/dhi.png "DHI Logo") diff --git a/app/public/data/gtif/markdown/SOL10_PVExisting.md b/app/public/data/gtif/markdown/SOL10_PVExisting.md deleted file mode 100644 index 69ce329302..0000000000 --- a/app/public/data/gtif/markdown/SOL10_PVExisting.md +++ /dev/null @@ -1,11 +0,0 @@ -## PV Detections - -Solar Roofs products are a cutting-edge solution for mapping and assessing the presence of photovoltaic (PV) panels on roofs. These products are based on the latest available airborne imagery with a spatial resolution of 20 cm, providing a high level of detail and accuracy. The Solar Roofs products are designed to provide building-level information about the presence of PV panels on roofs, including their size and potential electric power output. This information is generated by analysing the airborne imagery as well as digital surface model data and applying performance ratios and efficiency data for the PV panels. - - - -**Please note that the dataset provided covers the entire region of Austria (excluding Vienna). However, it may contain incorrect information regarding buildings within the Zell am See and Nordburgenland areas due to missing data. Please be aware that this dataset is subject to change and will be updated if additional airborne imagery becomes available.** - -
- - \ No newline at end of file diff --git a/app/public/data/gtif/markdown/SOL1_GRExisting.md b/app/public/data/gtif/markdown/SOL1_GRExisting.md deleted file mode 100644 index 86e97293cd..0000000000 --- a/app/public/data/gtif/markdown/SOL1_GRExisting.md +++ /dev/null @@ -1,7 +0,0 @@ -## Existing green rooftops - -Mapping of roofs which have a green roof installed. - -
- - diff --git a/app/public/data/gtif/markdown/SOL1_GRImpact.md b/app/public/data/gtif/markdown/SOL1_GRImpact.md deleted file mode 100644 index f06dbcb002..0000000000 --- a/app/public/data/gtif/markdown/SOL1_GRImpact.md +++ /dev/null @@ -1,7 +0,0 @@ -## Green roof impact score - -Describes the impact of retrofitting a house with a green roof based on land surface temperature and surrounding greeness. - -
- - diff --git a/app/public/data/gtif/markdown/SOL1_GRPotential.md b/app/public/data/gtif/markdown/SOL1_GRPotential.md deleted file mode 100644 index 5fc959ac56..0000000000 --- a/app/public/data/gtif/markdown/SOL1_GRPotential.md +++ /dev/null @@ -1,7 +0,0 @@ -## Green roof potential - -Green roof potential identifies all roofs that are suited for a green roof retorfitting solely looking at the slope of the roof. - -
- - diff --git a/app/public/data/gtif/markdown/SOL1_PVExisting.md b/app/public/data/gtif/markdown/SOL1_PVExisting.md deleted file mode 100644 index 0590fc9d15..0000000000 --- a/app/public/data/gtif/markdown/SOL1_PVExisting.md +++ /dev/null @@ -1,7 +0,0 @@ -## Existing photovoltaic panels - -Mapping of roofs which have a PV panel on top. - -
- - diff --git a/app/public/data/gtif/markdown/SOL1_TEP_HP.md b/app/public/data/gtif/markdown/SOL1_TEP_HP.md deleted file mode 100644 index acb80fbba9..0000000000 --- a/app/public/data/gtif/markdown/SOL1_TEP_HP.md +++ /dev/null @@ -1,7 +0,0 @@ -## Total electric power production potential - High performance - -Describes the potential electric power in MWh that can be produced considering that the PV has a high performance ratio [92%] and a high efficiency [23%]. - -
- - diff --git a/app/public/data/gtif/markdown/SOL1_TEP_LP.md b/app/public/data/gtif/markdown/SOL1_TEP_LP.md deleted file mode 100644 index 3f82d1a6c5..0000000000 --- a/app/public/data/gtif/markdown/SOL1_TEP_LP.md +++ /dev/null @@ -1,7 +0,0 @@ -## Total electric power production potential - Low performance - -Describes the potential electric power in MWh that can be produced considering that the PV has a low performance ratio [80%] and a low efficiency [15%]. - -
- - diff --git a/app/public/data/gtif/markdown/SOL1_TEP_RP.md b/app/public/data/gtif/markdown/SOL1_TEP_RP.md deleted file mode 100644 index 67be21fae3..0000000000 --- a/app/public/data/gtif/markdown/SOL1_TEP_RP.md +++ /dev/null @@ -1,7 +0,0 @@ -## Total electric power production potential - Regular performance - -Describes the potential electric power in MWh that can be produced considering that the PV has a regular performance ratio [86%] and a regular efficiency [19%]. - -
- - diff --git a/app/public/data/gtif/markdown/SOL_temp.md b/app/public/data/gtif/markdown/SOL_temp.md deleted file mode 100644 index d821835c04..0000000000 --- a/app/public/data/gtif/markdown/SOL_temp.md +++ /dev/null @@ -1,3 +0,0 @@ -Maximum Land Surface Temperatures for the year 2021 retrieved by Sentinel-3. - - \ No newline at end of file diff --git a/app/public/data/gtif/markdown/SPEI.md b/app/public/data/gtif/markdown/SPEI.md deleted file mode 100644 index 471202f90b..0000000000 --- a/app/public/data/gtif/markdown/SPEI.md +++ /dev/null @@ -1,3 +0,0 @@ -The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). - -Click on a feature on the map to see an extended time series. \ No newline at end of file diff --git a/app/public/data/gtif/markdown/SPI.md b/app/public/data/gtif/markdown/SPI.md deleted file mode 100644 index 3ad81ab818..0000000000 --- a/app/public/data/gtif/markdown/SPI.md +++ /dev/null @@ -1,3 +0,0 @@ -The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). - -Click on a feature on the map to see an extended time series. \ No newline at end of file diff --git a/app/public/data/gtif/markdown/VTT.md b/app/public/data/gtif/markdown/VTT.md deleted file mode 100644 index 8ff560c6fb..0000000000 --- a/app/public/data/gtif/markdown/VTT.md +++ /dev/null @@ -1,3 +0,0 @@ -### Disclaimer - -The forest parameter values are outputs of a data assimilation process combining EO-based estimates and a process based growth model initiated with EO based forest structure estimates and meteorological data. These prototype products have been created with limited input datasets and have not been validated. They should be considered merely as examples of the types of products that can be created. \ No newline at end of file diff --git a/app/public/data/gtif/markdown/dataInfo/AQ4.md b/app/public/data/gtif/markdown/dataInfo/AQ4.md deleted file mode 100644 index c18b60d7ae..0000000000 --- a/app/public/data/gtif/markdown/dataInfo/AQ4.md +++ /dev/null @@ -1,13 +0,0 @@ -|Product Name| Human Mobility Patterns | -| --- | --- | -| Valid range |