************** Remote Sensing ************** Articles & Tutorials #################### `Full course on spatial data `_ `Geospatial analysis `_ `Using GeoSpatial Data Analytics: A Friendly Guide to Folium and Rasterio `_ `Support Vector machines for classification in remote sensing `_ `Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series `_ `Satelite imagery access and analysis in Python & Jupyter `_ `Intro RSGISLib, GDAL, and ScikitLearn `_ `NDVI, NDBI & NDWI Calculation Using Landsat 7, 8 `_ `geemap documentation `_ `Earth Observation in Support of Malaria Control and Epidemiology: MALAREO Monitoring Approaches `_ `Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review `_ `Intro to Geographic Data Science (Colab notebooks, code) `_ `Quantitative Detection of Water Bodies using Deep Learning `_ `Understanding Image Data and Color Histograms in Satellite Imagery `_ `Swimming pool detection and classification using deep learning `_ `Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review `_ `Containing the spread of malaria with geospatial tech `_ `Data Science and Satellite Imagery `_ `Satellite imagery analysis with Python `_ `Precise Delineation of Small Water Bodies from Sentinel-1 Data using Support Vector Machine Classification `_ `Deep learning for satellite imagery via image segmentation `_ `Global surface water explorer `_ `How to Segment Buildings on Drone Imagery with Fast.ai & Cloud-Native GeoData Tools (CoLab Notebook here) `_ `Ilastik - interactive tool `_ `Deep Learning with Satellite Data `_ `Landsat `_ `Evaluation of a new 18-year MODIS-derived surface water fraction dataset T for constructing Mediterranean wetland open surface water dynamics `_ `10 Python image manipulation tools `_ `Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa `_ `Image Classification Techniques in Remote Sensing `_ `GEE `_ `Sentinel `_ `Satellite imagery access and analysis in Python & Jupyter notebooks `_ `Sentinel 2 Bands and Combinations `_ `LandCoverNet Dataset Documentation `_ `Previous computer vision road mapping project `_ `Predicting crop type with GEE `_ `Network analysis using osmnx `_ `How a team of scientists studying drought helped build the world’s leading famine prediction model `_ `NDVI, Mapping a Function over a Collection, Quality Mosaicking `_ `Using Satellite Imagery to Predict Changes in Income and Population `_ `Mapping New Informal Settlements for Humanitarian Aid through Machine Learning `_ `Visualizing Built-Up Areas Using Satellite Images `_ `Google Environmental Insights Explorer for solar rooftop potential `_ `A Beginner’s Guide to Segmentation in Satellite Images `_ `Neural Network for Satellite Data Classification Using Tensorflow in Python `_ `Deep learning for Geospatial data applications — Multi-label Classification `_ `Crop Classification with Multi-Temporal Satellite Imagery `_ `Deep learning for satellite imagery via image segmentation `_ `Applying Deep Learning on Satellite Imagery Classification. `_ `Metrics to Evaluate your Semantic Segmentation Model `_ `Creating training patches for Deep Learning Image Segmentation of Satellite (Sentinel 2) Imagery using the Google Earth Engine (GEE) `_ `How to create a DataBlock for Multispectral Satellite Image Segmentation with the Fastai-v2 `_ `Pan-European `_ `Data Science and Satellite Imagery `_ `How to use a pre-trained model (VGG) for image classification `_ `Understanding Image Data and Color Channels in Satellite Imagery `_ `Land-cover maps of Europe from the Cloud `_ `An Image Processing Tool to Generate Ground Truth Data from Satellite Images using Deep Learning `_ `Creating an Interactive Yield Prediction App Using Google Earth Engine and Jupyter Notebook `_ `Сrор field boundary detection: approaches overview and main challenges `_ `More accurate and flexible cloud masking for Sentinel-2 images `_ `Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: A case study from Bangladesh `_ `Multi-Class Metric Made Simple,Part I:Precision and Recall `_ `Application of NDVI on Pasture `_ `Vegetation indices and when to use them `_ `Dealing with Geospatial Raster Data in Python with Rasterio `_ `Use Google Earth Engine and Python API to Export Images to Roboflow `_ `Build Multi Label Image Classification Model with Python `_ `What is imbalance in image segmentation `_ `Image Segmentation With 5 Lines of Code `_ `Vegetation Index calculation from Satellite Imagery `_ Videos ###### `How to download ESA Sentinel 2 images `_ `Hands-on Satellite Imagery Analysis | Scipy 2019 Tutorial | Samapriya Roy, Sara Safavi `_ `Hands-on Satellite Imagery Analysis | SciPy 2018 Tutorial | Sara Safavi, Dana Bauer `_ `Satellite images analysis `_ `GEE and QGIS intro `_ Repos ##### `GEE guide `_ `WaterNet `_ `Python Geospatial `_ `GEE ee-api setup on colab `_ `Sat image processing `_ `Resources for performing deep learning (DL) on satellite imagery `_ `Surface Water Mapping by Deep Learning `_ `Deep Water Map `_ `Satellite images datasets `_ `Crop type mapping `_ `Satellite imagery dataset `_ `Combining satellite imagery and machine learning to predict poverty `_ `Code to download satellite images for some Sub-Saharan African cities `_ `QuickOSM allows you to work quickly with OSM data in QGIS thanks to Overpass API. `_ `A repo that monitors surface water levels of waterbodies across the globe - Water Observatory Backend `_ Research Papers ############### `Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Models `_ `Deep Water Maps `_ `Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree `_ `Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1 `_ `Potential of Large-Scale Inland Water Body Mapping from Sentinel-1 / 2 Data on Example of Bavaria’s Lakes and Rivers `_ `Detection and Characterization of Small Water Bodies `_ `Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning `_ `Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification `_ `Deep Landscape Features for Improving Vector-borne Disease Prediction `_ `Automated Mapping of Water Bodies Using Landsat Multispectral Data `_ `Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model `_ `Automatic and Unsupervised Water Body Extraction Based on Spectral-Spatial Features Using GF-1 Satellite Imagery `_ `Land cover classification from fused DSM and UAV images using convolutional neural networks `_ `EXPLORING CAPABILITIES OF SENTINEL-2 FOR VEGETATION MAPPING USING RANDOM FOREST `_ `ClusterNet: unsupervised generic feature learning for fast interactive satellite image segmentation `_ `Desert locust detection using Earth observation satellite data in Mauritania `_ `Locusts and remote sensing: a review `_ `Joint use of Sentinel-1 and Sentinel-2 for land cover classification: A machine learning approach `_ `Exploring Capabilities Of Sentinel-2 For Vegetation Mapping Using Random Forest `_ `Climate Data Guide `_ `Predicting the Normalized Difference Vegetation Index (NDVI) by training a crop growth model with historical data `_ `Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data `_ `Wheat Crop Yield Prediction Using Deep LSTM Model `_ `Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data `_ `Measuring Vegetation from Satellite Imagery with NDVI `_ `Handbook of Drought Indicators and Indices `_ `Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali `_ `Weekly supervised deep learning for segmentation of remote sensing imagery `_ `NDVI from other sensors `_ `Water body Extraction Methods Study Based on RS and GIS `_ `Seeing through the clouds with DeepWaterMap `_ `Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands `_ `Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images `_ `Ensemble Classification Technique for Water Detection in Satellite Images `_ `A Novel Water Change Tracking Algorithm for Dynamic Mapping of Inland Water Using Time-Series Remote Sensing Imagery `_ `Monitoring the Water Quality of Small Water Bodies Using High Resolution Remote Data Sensing `_ `Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats `_ `Automatic and Unsupervised Water Body Extraction Based on Spectral-Spatial Features Using GF-1 Satellite Imagery `_ `The application of drones for mosquito larval habitat identification in rural environments `_ `Water Level Measurements from Drones: A Pilot Case Study at a Dam Site `_ `Data Fusion for Multi-Temporal Mapping of Built-Up Areas in Sub-Saharan Africa `_ `Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review `_ `Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image `_ `Modelling of Malaria Hotspots `_ `Techniques of Artificial Intelligence for Space Applications `_