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Samuel ROESLIN's Projects

building-viewer icon building-viewer

Example of a building in a compelling website built on top of ArcGIS JavaScript API

ccxt icon ccxt

A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges

drought-svi icon drought-svi

Hands-on instructions on how to use satellite information for drought monitoring

eoreader icon eoreader

Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.

gitfolio icon gitfolio

:octocat: personal website + blog for every github user

gpt3-sandbox icon gpt3-sandbox

The goal of this project is to enable users to create cool web demos using the newly released OpenAI GPT-3 API with just a few lines of Python.

landslides icon landslides

This repository is a collection of basic scripts - explained in English and Spanish - for analysing landslides using space-based data in Google Earth Engine (GGE), Sentinel Playground or EO Browser. It aims to facilitate working with big data in the cloud as an alternative to using desktop software.

ml4floods icon ml4floods

An ecosystem of data, models and code pipelines to tackle flooding with ML

national-flood-insurance-program icon national-flood-insurance-program

Repository based on the OpenFEMA data for the National Flood Insurance Program (NFIP). Exploration of the opportunity to derive insights using this dataset.

qgis-earthengine-fires-monitoring icon qgis-earthengine-fires-monitoring

The following is a Python example of the Google Earth Engine API in QGIS, for monitoring and tracking wildfires. In addition, it includes some examples adapted from the examples developed as recommended practices of the UN-SPIDER program.

radar-based-flood-mapping icon radar-based-flood-mapping

This repository contains a Jupyter Notebook for automatic flood extent mapping using space-based information.

site icon site

Course materials for the Automating GIS processes -course, University of Helsinki, Finland

torchgeo icon torchgeo

TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data

unosat-ai-based-rapid-mapping-service icon unosat-ai-based-rapid-mapping-service

This GitHub repository contains the machine learning models described in Edoardo Nemnni, Joseph Bullock, Samir Belabbes, Lars Bromley Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery.

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