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Found 5 Skills
Geospatial Analysis provides workflows for satellite imagery processing, GIS operations with GeoPandas, spatial statistics, and Earth observation data analysis.
Builds Geographically Weighted Regression (GWR) workflows in CARTO. Triggers when the user mentions GWR, geographically weighted regression, spatially varying relationships, local regression, local coefficients, spatial regression, "what drives X in different areas", "why do prices vary spatially", "local factors affecting Y", varying coefficients, coefficient maps, spatial non-stationarity, or wants to model how the relationship between a dependent variable and predictors changes across geography. Produces per-cell regression coefficients that reveal how predictor importance shifts from place to place.
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
Use when "GeoPandas", "geospatial", "GIS", "shapefile", "GeoJSON", or asking about "spatial analysis", "coordinate transformation", "spatial join", "choropleth map", "buffer analysis", "geographic data", "map visualization"
Determine where a photo was taken (photo location guessing / internet sleuthing / GeoGuessr-style play / infer capture time). Given one or several photos, a single command first performs metadata, OCR, and reverse image search (`intake.py`), records clues and candidates on the candidate board (`board.py`) for script ranking and next-step suggestions; lookup-table clues use `clues.py`; satellite and street view both follow the principle that the machine ranks first and humans only look at the top hits (`sat_scan.py`, `match.py`); every conclusion is checked against real data and output with coordinates + error radius, evidence montage, and tiered confidence. Geolocate or chronolocate a photo with tool-verified reasoning — EXIF, OCR, reverse image search (Baidu/Yandex), lookup tables (plates, area codes, calling codes, driving side, territories), candidate board with likelihood ranking, sun and shadow math, OSM Overpass, CLIP-ranked satellite scan, DINOv2+SIFT-ranked street view matching, DEM skyline rendering. Use when the user shares a photo and asks where this is / where it was taken / help me geolocate this photo / internet-sleuth this / what time this was taken / where was this taken / geolocate this.