planetary-computer

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.

Spatiotemporal tools to make tif generation from Sentinel-2 satellites easier.

Visualizes environmental changes in a region over a period of time using OlmoEarth embeddings.

FastAPI service for downloading satellite imagery from Microsoft Planetary Computer. Deployed serverless on AWS Lambda with Docker.

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.

Linear-probe land-cover classifier on precomputed Tessera (Sentinel-1+2 foundation model) embeddings — one tile, one day, CPU-only.

Sentinel-2 satellite change detection with NDVI, NDWI and NBR analysis, cloud masking, GeoTIFF export and real-world validation.

Sieve polygons by fractional class coverage of categorical rasters, read directly from Cloud-Optimized GeoTIFFs over HTTP.

Sentinel-1 + Sentinel-2 forest-disturbance monitoring: STAC-native Dask cube, omnibus Wishart change detection and error-adjusted area with confidence intervals.

Observer based point cloud viewshed

ALOS AW3D30 watershed delineation for Launglon Township, Myanmar, using Microsoft Planetary Computer, D8 routing, GeoJSON exports, and publication maps.

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.