Building detection from the SpaceNet dataset by using Mask RCNN.
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Updated
Apr 1, 2021 - Jupyter Notebook
Building detection from the SpaceNet dataset by using Mask RCNN.
Building detection from the SpaceNet dataset using UNet.
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.
Deep Learning Based Building Detection with Satellite Imagery
Building detection model with YOLOv10 on UAVOD-10 dataset
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.
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