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Name: Remote Sensing AI
Type: Organization
Name: Remote Sensing AI
Type: Organization
AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.
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"
This repository contains the code, test patches and weights for the paper [Deep Learning based road extraction from historical maps]
Land Use / Land Cover Mappings Using Deep Learning Methods
A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation
A benchmark dataset for deep learning-based airplane detection: HRPlanes
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).
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.
Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
This repository contains the code for the paper "A MULTI-TASK DEEP LEARNING FRAMEWORK FOR BUILDING FOOTPRINT SEGMENTATION"
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs.
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.
All image quality metrics you need in one package.
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.
A benchmark dataset for deep learning-based tree detection: VHRTrees
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.