Tools tagged with "PlanetScope"

Planet Labs provides daily satellite imagery and Earth data analytics. Its insights empower global decisions and actions with a multidimensional view of our changing planet, offering comprehensive monitoring and tasking solutions.

This blog provides benchmarks and tips for working with big data, cloud platforms, and various geospatial datasets. It features in-depth analyses of large-scale open data, satellite imagery, and GIS software applications. The content covers a range of topics from data center locations to global building footprints and advanced imagery processing.

OmniCloudMask is a Python library designed for state-of-the-art cloud and cloud shadow segmentation in high to moderate resolution satellite imagery. It offers higher accuracy and supports a wide range of resolutions, sensors, and processing levels, validated on data from Sentinel-2, PlanetScope, Landsat, and Maxar.

The FloodPlanet dataset provides manually labeled inundation data, meticulously collected for advancing flood detection research. It serves as a crucial resource for training and validating deep learning models, utilizing high-resolution imagery from PlanetScope, Sentinel-1, and Sentinel-2 satellites. This version 1.0 dataset, contributed by Zhijie(JJ) Zhang and collaborators, is a valuable asset for the geospatial community.

This repository offers a global dataset of building density and height, generated quarterly by applying machine learning models to PlanetScope basemaps. It provides a global public layer and detailed data for five high-growth locations, distributed as Cloud-Optimized GeoTIFFs (COGs). The resource supports both data exploration and model training for users interested in urban development analysis.