Building detection from the SpaceNet dataset by using Mask RCNN.

The P3 Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization

Building detection from the SpaceNet dataset using UNet.

Deep Learning Based Building Detection with Satellite Imagery

Building detection model with YOLOv10 on UAVOD-10 dataset

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.

Group Project for USTH Deep Learning Course 2023, using Faster R-CNN with pretrained model weight (gone wrong)

An AI-powered web application for detecting and extracting building footprints from satellite imagery.

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.

Preprocessed and split version of the SSBH remote sensing dataset for building height estimation, including RGB composites, height maps, and building masks with train/valid/test manifests and ready-to-train scripts.