Pinned Repositories
aitlas
AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.
Automatic-Road-Extraction-from-Historical-Maps-using-Deep-Learning-Techniques
This repository contains code for the paper "Automatic Road Extraction from Historical Maps using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II map"
DL-based-road-extraction-from-historical-maps
This repository contains the code, test patches and weights for the paper [Deep Learning based road extraction from historical maps]
DL-LULC-Mapping
Land Use / Land Cover Mappings Using Deep Learning Methods
HRPlanes
A benchmark dataset for deep learning-based airplane detection: HRPlanes
Istanbul-Building-Dataset-Benchmark-Building-Extraction-Dataset-and-DL-Models
This repo contains weights of Unet++ model with SE-ResNeXt101 encoder trained with Istanbul, Inria and Massachusetts datasets seperately. Trainings have been realized using PyTorch and segmentation models library (https://github.com/qubvel/segmentation_models.pytorch) We also provide an inference notebook to run prediction on GeoTiff images. This notebook also outputs prediction images as GeoTiff.
LULCMapping-WV3images-CORINE-DLMethods
Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
MTL-Based-DL-Framework-for-Building-Footprint-Segmentation
This repository contains the code for the paper "A MULTI-TASK DEEP LEARNING FRAMEWORK FOR BUILDING FOOTPRINT SEGMENTATION"
PanColorGAN-VHR-Satellite-Images
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs. Pretrained Weights and GAN training parts of the code can be found in this repo.
VHRShips
This study focuses on all stages of ship classification in the optical satellite images. The proposed “Hierarchical Design (HieD)” approach, which is based on deep learning techniques, performs Detection, Localization, Recognition and Identification (DLRI) of the ships in the optical satellite images. HieD is an end-to-end approach which allows the optimization of each stage of the DLRI independently. A unique and rich ship dataset (High Resolution Ships, HRShips), which is formed by the Google Earth Pro software, is used in this study. While Xception network is used in detection, recognition and identification stages; YOLOv4 is preferred for the localization of the ships.
Remote Sensing AI's Repositories
RSandAI/MTL-Based-DL-Framework-for-Building-Footprint-Segmentation
This repository contains the code for the paper "A MULTI-TASK DEEP LEARNING FRAMEWORK FOR BUILDING FOOTPRINT SEGMENTATION"
RSandAI/DL-based-road-extraction-from-historical-maps
This repository contains the code, test patches and weights for the paper [Deep Learning based road extraction from historical maps]
RSandAI/LULCMapping-WV3images-CORINE-DLMethods
Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
RSandAI/VHRTrees
A benchmark dataset for deep learning-based tree detection: VHRTrees
RSandAI/HistRoadSegformer
RSandAI/Istanbul-Building-Dataset-Benchmark-Building-Extraction-Dataset-and-DL-Models
This repo contains weights of Unet++ model with SE-ResNeXt101 encoder trained with Istanbul, Inria and Massachusetts datasets seperately. Trainings have been realized using PyTorch and segmentation models library (https://github.com/qubvel/segmentation_models.pytorch) We also provide an inference notebook to run prediction on GeoTiff images. This notebook also outputs prediction images as GeoTiff.
RSandAI/Automatic-Road-Extraction-from-Historical-Maps-using-Deep-Learning-Techniques
This repository contains code for the paper "Automatic Road Extraction from Historical Maps using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II map"
RSandAI/DL-LULC-Mapping
Land Use / Land Cover Mappings Using Deep Learning Methods
RSandAI/HRPlanes
A benchmark dataset for deep learning-based airplane detection: HRPlanes
RSandAI/PanColorGAN-VHR-Satellite-Images
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs. Pretrained Weights and GAN training parts of the code can be found in this repo.
RSandAI/.github
RSandAI/aitlas
AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.
RSandAI/HRPlanes-HighResolution-Planes-Benchmark-Dataset
High Resolution Planes Benchmark Dataset-HRPlanes. This repo contains weights of YOLOv4 and Faster R-CNN networks trained with HRPlanes dataset. YOLOv4 training have been performed using Darknet (https://github.com/AlexeyAB/darknet). Faster R-CNN have been trained using TensorFlow Object Detection API v1.13 (https://github.com/tensorflow/models/tree/r1.13.0).
RSandAI/sewar
All image quality metrics you need in one package.
RSandAI/VHRShips
This study focuses on all stages of ship classification in the optical satellite images. The proposed “Hierarchical Design (HieD)” approach, which is based on deep learning techniques, performs Detection, Localization, Recognition and Identification (DLRI) of the ships in the optical satellite images. HieD is an end-to-end approach which allows the optimization of each stage of the DLRI independently. A unique and rich ship dataset (High Resolution Ships, HRShips), which is formed by the Google Earth Pro software, is used in this study. While Xception network is used in detection, recognition and identification stages; YOLOv4 is preferred for the localization of the ships.
RSandAI/HexaLCSeg
A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation
RSandAI/PanColorGAN
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs.