An open API service providing repository metadata for many open source software ecosystems.

Topic: "building-detection"

Mstfakts/Building-Detection-MaskRCNN

Building detection from the SpaceNet dataset by using Mask RCNN.

Language: Jupyter Notebook - Size: 16 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 191 - Forks: 20

Rim-chan/SpaceNet7-Buildings-Detection

Building detection from the SpaceNet dataset using UNet.

Language: Python - Size: 1.14 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 9 - Forks: 3

Picogeek06/FinalYear_Project

Deep Learning Based Building Detection with Satellite Imagery

Language: Jupyter Notebook - Size: 3.15 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 4 - Forks: 1

dolphinium/uav-building-detection

Building detection model with YOLOv10 on UAVOD-10 dataset

Language: Python - Size: 114 MB - Last synced at: about 2 months ago - Pushed at: 2 months ago - Stars: 2 - Forks: 0

naayem/DeepLearning-BuildingSegmentation

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

Language: Jupyter Notebook - Size: 191 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

raphaelsulzer/PixelsPointsPolygons

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

Language: Python - Size: 18.3 MB - Last synced at: 13 days ago - Pushed at: 13 days ago - Stars: 0 - Forks: 0

Ruthik27/Building_Detection

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.

Language: Python - Size: 60.4 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

MuhammedM294/AerialSeg

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.

Language: Jupyter Notebook - Size: 112 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0