Building detection from the SpaceNet dataset by using Mask RCNN.

Building detection from the SpaceNet dataset using UNet.

Deep Learning Based Building Detection with Satellite Imagery

This initiative leverages cutting-edge machine learning technique such as Mask R-CNN to automate the identification of buildings in satellite images after disasters. Employing high-resolution Maxar imagery, our models efficiently and accurately pinpoint affected structures, enhancing the speed and effectiveness of emergency responses.

A deep learning project utilizing Mask R-CNN for building instance segmentation, openings detection, and building type classification.

This repository provides an implementation of semantic segmentation for road networks using PyTorch and the U-Net architecture. It focuses specifically on processing aerial images from the Massachusetts dataset.