SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions

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Created almost 6 years ago
Updated over 4 years ago

SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions

SpaceNetChallenge

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Winning algorithms for the SpaceNet 4: Off-Nadir Building Footprint Detection Challenge

SpaceNet Challenge Round 4: Off-Nadir Buildings

Competitor solutions

Summary

The five subdirectories in this repository comprise the code for the winning solutions of SpaceNet 4: Off-Nadir Building Detection hosted by TopCoder. Each subdirectory contains the competitors' written descriptions of their solution to the challenge. See the blog post on CosmiQ Works' blog The DownlinQ for an additional summary.

Coming soon

Links to download the competitors' trained models

Questions about SpaceNet? Check out our website at https://spacenet.ai.

utilities

Packages intended to assist in the preprocessing of SpaceNet satellite imagery data corpus to a format that is consumable by machine learning algorithms.

RoadDetector

Winning Solutions from SpaceNet Road Detection and Routing Challenge

BuildingDetectors_Round2

Building detector algorithms from second SpaceNet Challenge

BuildingDetectors

Winning building footprint detector implementations from the SpaceNet challenges.

SpaceNet8

Algorithmic baseline for SpaceNet 8 Challenge

SpaceNet_SAR_Buildings_Solutions

The winning solutions for the SpaceNet 6 Challenge

Jupyter Notebook

SpaceNet7_Multi-Temporal_Solutions

SpaceNet_Optimized_Routing_Solutions

SAR-RGB-DataFusion

A small codebase featuring a few elementary methods for SAR and Optical imagery data fusion

Jupyter Notebook

BuildingDetectorVisualizer

The purpose of the visualizer application is to let you view 3-band and 8-band images, view ground truth building footprints and your solution's building footprint as overlays on these images, compare truth to solution and calculate your solution's score.

stac-implementation

Tools for Implementation of STAC SPEC for SpaceNet dataset https://github.com/radiantearth/stac-spec/tree/dev

Jupyter Notebook

ml-export-tool