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Add GeoMaster: Comprehensive Geospatial Science Skill
- Added SKILL.md with installation, quick start, core concepts, workflows - Added 12 reference documentation files covering 70+ topics - Includes 500+ code examples across 7 programming languages - Covers remote sensing, GIS, ML/AI, 30+ scientific domains - MIT License Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Co-Authored-By: Dr. Umair Rabbani <umairrs@gmail.com>
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urabbani
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scientific-skills/geomaster/references/data-sources.md
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scientific-skills/geomaster/references/data-sources.md
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# Geospatial Data Sources
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Comprehensive catalog of satellite imagery, vector data, and APIs for geospatial analysis.
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## Satellite Data Sources
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### Sentinel Missions (ESA)
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| Platform | Resolution | Coverage | Access |
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|----------|------------|----------|--------|
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| **Sentinel-2** | 10-60m | Global | https://scihub.copernicus.eu/ |
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| **Sentinel-1** | 5-40m (SAR) | Global | https://scihub.copernicus.eu/ |
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| **Sentinel-3** | 300m-1km | Global | https://scihub.copernicus.eu/ |
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| **Sentinel-5P** | Various | Global | https://scihub.copernicus.eu/ |
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```python
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# Access via Sentinelsat
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from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
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api = SentinelAPI('user', 'password', 'https://scihub.copernicus.eu/dhus')
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# Search
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products = api.query(geojson_to_wkt(aoi_geojson),
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date=('20230101', '20231231'),
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platformname='Sentinel-2',
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cloudcoverpercentage=(0, 20))
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# Download
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api.download_all(products)
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```
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### Landsat (USGS/NASA)
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| Platform | Resolution | Coverage | Access |
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|----------|------------|----------|--------|
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| **Landsat 9** | 30m | Global | https://earthexplorer.usgs.gov/ |
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| **Landsat 8** | 30m | Global | https://earthexplorer.usgs.gov/ |
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| **Landsat 7** | 15-60m | Global | https://earthexplorer.usgs.gov/ |
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| **Landsat 5-7** | 30-60m | Global | https://earthexplorer.usgs.gov/ |
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### Commercial Satellite Data
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| Provider | Platform | Resolution | API |
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|----------|----------|------------|-----|
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| **Planet** | PlanetScope, SkySat | 0.5-3m | planet.com |
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| **Maxar** | WorldView, GeoEye | 0.3-1.2m | maxar.com |
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| **Airbus** | Pleiades, SPOT | 0.5-2m | airbus.com |
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| **Capella** | Capella-2 (SAR) | 0.5-1m | capellaspace.com |
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## Elevation Data
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| Dataset | Resolution | Coverage | Source |
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|---------|------------|----------|--------|
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| **AW3D30** | 30m | Global | https://www.eorc.jaxa.jp/ALOS/en/aw3d30/ |
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| **SRTM** | 30m | 56°S-60°N | https://www.usgs.gov/ |
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| **ASTER GDEM** | 30m | 83°S-83°N | https://asterweb.jpl.nasa.gov/ |
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| **Copernicus DEM** | 30m | Global | https://copernicus.eu/ |
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| **ArcticDEM** | 2-10m | Arctic | https://www.pgc.umn.edu/ |
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```python
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# Download SRTM via API
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import elevation
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# Download SRTM 1 arc-second (30m)
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elevation.clip(bounds=(-122.5, 37.7, -122.3, 37.9), output='srtm.tif')
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# Clean and fill gaps
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elevation.clean('srtm.tif', 'srtm_filled.tif')
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```
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## Land Cover Data
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| Dataset | Resolution | Classes | Source |
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|---------|------------|---------|--------|
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| **ESA WorldCover** | 10m | 11 classes | https://worldcover2021.esa.int/ |
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| **ESRI Land Cover** | 10m | 10 classes | https://www.esri.com/ |
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| **Copernicus Global** | 100m | 23 classes | https://land.copernicus.eu/ |
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| **MODIS MCD12Q1** | 500m | 17 classes | https://lpdaac.usgs.gov/ |
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| **NLCD (US)** | 30m | 20 classes | https://www.mrlc.gov/ |
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## Climate & Weather Data
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### Reanalysis Data
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| Dataset | Resolution | Temporal | Access |
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|---------|------------|----------|--------|
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| **ERA5** | 31km | Hourly (1979+) | https://cds.climate.copernicus.eu/ |
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| **MERRA-2** | 50km | Hourly (1980+) | https://gmao.gsfc.nasa.gov/ |
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| **JRA-55** | 55km | 3-hourly (1958+) | https://jra.kishou.go.jp/ |
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```python
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# Download ERA5 via CDS API
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import cdsapi
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c = cdsapi.Client()
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c.retrieve(
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'reanalysis-era5-single-levels',
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{
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'product_type': 'reanalysis',
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'variable': '2m_temperature',
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'year': '2023',
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'month': '01',
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'day': '01',
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'time': '12:00',
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'area': [37.9, -122.5, 37.7, -122.3],
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'format': 'netcdf'
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},
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'era5_temp.nc'
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)
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```
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## OpenStreetMap Data
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### Access Methods
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```python
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# Via OSMnx
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import osmnx as ox
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# Download place boundary
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gdf = ox.geocode_to_gdf('San Francisco, CA')
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# Download street network
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G = ox.graph_from_place('San Francisco, CA', network_type='drive')
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# Download building footprints
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buildings = ox.geometries_from_place('San Francisco, CA', tags={'building': True})
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# Via Overpass API
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import requests
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overpass_url = "http://overpass-api.de/api/interpreter"
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query = """
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[out:json];
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way["highway"](37.7,-122.5,37.9,-122.3);
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out geom;
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"""
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response = requests.get(overpass_url, params={'data': query})
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data = response.json()
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```
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## Vector Data Sources
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### Natural Earth
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```python
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import geopandas as gpd
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# Admin boundaries (scale: 10m, 50m, 110m)
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countries = gpd.read_file('https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_countries.zip')
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urban_areas = gpd.read_file('https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_urban_areas.zip')
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ports = gpd.read_file('https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_ports.zip')
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```
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### Other Sources
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| Dataset | Type | Access |
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|---------|------|--------|
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| **GADM** | Admin boundaries | https://gadm.org/ |
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| **HydroSHEDS** | Rivers, basins | https://www.hydrosheds.org/ |
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| **Global Power Plant** | Power plants | https://datasets.wri.org/ |
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| **WorldPop** | Population | https://www.worldpop.org/ |
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| **GPW** | Population | https://sedac.ciesin.columbia.edu/ |
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| **HDX** | Humanitarian data | https://data.humdata.org/ |
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## APIs
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### Google Maps Platform
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```python
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import requests
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# Geocoding
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url = "https://maps.googleapis.com/maps/api/geocode/json"
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params = {
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'address': 'Golden Gate Bridge',
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'key': YOUR_API_KEY
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}
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response = requests.get(url, params=params)
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data = response.json()
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location = data['results'][0]['geometry']['location']
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```
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### Mapbox
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```python
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# Geocoding
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import requests
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url = "https://api.mapbox.com/geocoding/v5/mapbox.places/Golden%20Gate%20Bridge.json"
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params = {'access_token': YOUR_ACCESS_TOKEN}
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response = requests.get(url, params=params)
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data = response.json()
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```
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### OpenWeatherMap
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```python
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# Current weather
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url = "https://api.openweathermap.org/data/2.5/weather"
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params = {
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'lat': 37.7,
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'lon': -122.4,
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'appid': YOUR_API_KEY
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}
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response = requests.get(url, params=params)
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weather = response.json()
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```
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## Data APIs in Python
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### STAC (SpatioTemporal Asset Catalog)
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```python
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import pystac_client
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# Connect to STAC catalog
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catalog = pystac_client.Client.open("https://earth-search.aws.element84.com/v1")
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# Search
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search = catalog.search(
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collections=["sentinel-2-l2a"],
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bbox=[-122.5, 37.7, -122.3, 37.9],
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datetime="2023-01-01/2023-12-31",
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query={"eo:cloud_cover": {"lt": 20}}
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)
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items = search.get_all_items()
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```
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### Planetary Computer
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```python
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import planetary_computer
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import pystac_client
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catalog = pystac_client.Client.open(
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"https://planetarycomputer.microsoft.com/api/stac/v1",
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modifier=planetary_computer.sign_inplace
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)
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# Search and sign items
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items = catalog.search(...)
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signed_items = [planetary_computer.sign(item) for item in items]
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```
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## Download Scripts
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### Automated Download Script
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```python
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from sentinelsat import SentinelAPI
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import rasterio
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from rasterio.warp import calculate_default_transform, reproject, Resampling
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import os
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def download_and_process_sentinel2(aoi, date_range, output_dir):
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"""
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Download and process Sentinel-2 imagery.
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"""
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# Initialize API
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api = SentinelAPI('user', 'password', 'https://scihub.copernicus.eu/dhus')
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# Search
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products = api.query(
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aoi,
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date=date_range,
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platformname='Sentinel-2',
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processinglevel='Level-2A',
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cloudcoverpercentage=(0, 20)
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)
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# Download
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api.download_all(products, directory_path=output_dir)
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# Process each product
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for product in products:
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product_path = f"{output_dir}/{product['identifier']}.SAFE"
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processed = process_sentinel2_product(product_path)
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save_rgb_composite(processed, f"{output_dir}/{product['identifier']}_rgb.tif")
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def process_sentinel2_product(product_path):
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"""Process Sentinel-2 L2A product."""
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# Find 10m bands (B02, B03, B04, B08)
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bands = {}
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for band_id in ['B02', 'B03', 'B04', 'B08']:
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band_path = find_band_file(product_path, band_id, resolution='10m')
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with rasterio.open(band_path) as src:
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bands[band_id] = src.read(1)
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profile = src.profile
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# Stack bands
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stacked = np.stack([bands['B04'], bands['B03'], bands['B02']]) # RGB
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return stacked, profile
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```
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## Data Quality Assessment
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```python
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def assess_data_quality(raster_path):
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"""
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Assess quality of geospatial raster data.
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"""
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import rasterio
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import numpy as np
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with rasterio.open(raster_path) as src:
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data = src.read()
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profile = src.profile
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quality_report = {
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'nodata_percentage': np.sum(data == src.nodata) / data.size * 100,
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'data_range': (data.min(), data.max()),
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'mean': np.mean(data),
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'std': np.std(data),
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'has_gaps': np.any(data == src.nodata),
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'projection': profile['crs'],
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'resolution': (profile['transform'][0], abs(profile['transform'][4]))
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}
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return quality_report
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```
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For data access code examples, see [code-examples.md](code-examples.md).
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