Satellite image time series in R
A Python package for simple STAC queries
Streamlit web app 🎈 for creating 3D-printable models of the earth 🌍 surface based on mapa
Figuring out what the hottest villages in Kerala are with the help of Microsoft's Planetary Computer
Prefect integrations with Microsoft Planetary Computer.
The Microsoft Planetary Computer Catalog in CSV format
Client-side web application for exploring satellite imagery from Microsoft Planetary Computer. Supports Sentinel-2, Landsat, SAR, DEM, and MODIS with interactive visualization, measurement tools, and multi-band selection.
3rd place, Gemini Award — “Transforming Enterprise Through AI” lablab.ai hackathon. Autonomous freshwater monitoring with Sentinel‑2 imagery, deterministic spectral indices, and a Gemini multi‑agent workflow for risk triage and citizen‑friendly reports.
Visualizes environmental changes in a region over a period of time using OlmoEarth embeddings.
Spatiotemporal tools to make tif generation from Sentinel-2 satellites easier.
Sentinel-1 + Sentinel-2 forest-disturbance monitoring: STAC-native Dask cube, omnibus Wishart change detection and error-adjusted area with confidence intervals.
Part of NASA Harvest's RISE. Sat imagery download, training data creation, model deploy, STAC + COGs output. Can be run locally and on Azure ML.
Observer based point cloud viewshed
Linear-probe land-cover classifier on precomputed Tessera (Sentinel-1+2 foundation model) embeddings — one tile, one day, CPU-only.
FastAPI service for downloading satellite imagery from Microsoft Planetary Computer. Deployed serverless on AWS Lambda with Docker.
Multi-decadal clean-ice and nival-line retrieval from STAC-indexed Landsat/Sentinel-2 archives: band-ratio segmentation, majority-rule epoch compositing, robust non-parametric trends, cross-sensor agreement and terrain-irradiance attribution. Sites resolved at runtime.
A multi-modal machine learning solution to forecast water quality parameters in South Africa rivers using Landsat satellite imagery (STAC API), TerraClimate meteorological data, and Snowflake (Snowpark ML). Developed for the EY AI & Data Challenge 2026.
Hybrid ML + GIS pipeline for wildfire vegetation risk: U-Net segmentation on Sentinel-2 with Dynamic World labels, fused with line-distance and slope to produce a tunable risk raster. Validated across three external California AOIs.
🛰️ Веб-сервис космического мониторинга здоровья полей: Sentinel-2, ансамбль EVI+MSAVI2, карта-светофор за 10 секунд в браузере
Heat, mortality and who lives where it's hottest in Hessen, 2000–2025 (Hitzerisiko Hessen). Open data from DWD, Destatis, Zensus 2022 and Landsat; heat-related deaths checked against RKI's estimates; surface heat in its five cities mapped on the 100 m census grid. Hypotheses and data rules fixed in docs/DESIGN.md before anything was modelled.
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