landsat-8
There are 7 repositories under landsat-8 topic.
- awesome-spectral-indices
awesome-spectral-indices / awesome-spectral-indices
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
Language:Python 867 - Language:Jupyter Notebook 696
pylandtemp / pylandtemp
Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.
Language:Python 172- deck.gl-raster
kylebarron / deck.gl-raster
deck.gl layers and WebGL modules for client-side satellite imagery analysis
Language:JavaScript 85 qzhang95 / PSTCR
Q. Zhang, Q. Yuan, J. Li, Z. Li, H. Shen, and L. Zhang, "Thick Cloud and Cloud Shadow Removal in Multitemporal Images using Progressively Spatio-Temporal Patch Group Learning", ISPRS Journal, 2020.
Language:MATLAB 63- Language:C 62
olivierhagolle / Start_maja
To process a Sentinel-2 time series with MAJA cloud detection and atmospheric correction processor
Language:Python 51- landsat8image
v0di / landsat8image
A simple python script that, given a location and a date, uses the Nasa Earth API to show a photo taken by the Landsat 8 satellite. The script must be executed on the command-line.
Language:Python 44 RAJohansen / waterquality
Package designed to detect and quantify water quality and cyanobacterial harmful algal bloom (CHABs) from remotely sensed imagery
Language:R 42- landsat8.earth
2D/3D WebGL Landsat 8 satellite image analysis
Language:JavaScript 40 multiply-org / atmospheric_correction
Sentinel 2 and Landsat 8 Atmospheric correction
Language:C 35eupassarinho / GoogleEarthEngine-sentinel1-vegetation-indices
It contains Google Earth Engine codes (JavaScript API) to process Sentinel-1 and Landsat 8 images to compute SAR and optical vegetation indices.
Language:JavaScript 31amanbasu / wildfire-detection
Using Vision Transformers for enhanced wildfire detection in satellite images
Language:Python 21calekochenour / gee-vegetation-change
Reproducible remote sensing analysis using Google Earth Engine (GEE) to identify vegetation change in Columbia.
Language:Jupyter Notebook 18GenericMappingTools / RemoteS.jl
Remote sensing data processing
Language:Julia 18AlexeyPechnikov / satellite-spectrogram
Compare Spectrograms of Hyperspectral and Multispectral Satellite Missions
Language:Jupyter Notebook 15Auto-updating global Landsat 8 mosaic of Cloud-Optimized GeoTIFFs from SNS notifications
Language:Python 15- Language:Python 14
NASA-DEVELOP / ORCAA
The Optical Reef and Coastal Area Assessment (ORCAA) tool in Google Earth Engine allows users to monitor, track, and evaluate water parameters in the Belize and Honduras Barrier Reefs from January 2013 to present using Landsat 8, Sentinel-2, and Aqua/Terra MODIS imagery.
mohammad-aghdaminia / sea-land-segmentation-coastline-extraction
Python code for paper "Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net", JAG 2022
Language:Jupyter Notebook 12stevinc / EGNNA_WND
Pytorch code for estimating the presence of the West Nile Disease employing Graph Neural network
Language:Python 12The 2020 Spring Great Lakes Water Resources II adapted the Wetland Extent Tool (WET) to create WET 2.0, which is a tool with a Graphical User Interface (GUI) that automates wetland classification for the entire Great Lakes Basin using Sentinel-1 C-SAR, Landsat 8 OLI, Sentinel-2 MSI, and Dynamic Surface Water Extent (DSWE).
saraivaufc / instance-segmentation-maskrcnn
Instance segmentation of center pivot irrigation system in Brazil using Landsat images and Convolutional Neural Network
Language:Jupyter Notebook 10ElementMo / RSIP
A module to process LandSat8 Remote Sense Images. Features: NDBI NDVI NDWI calculation; LandSat8 BandMerge(GIS info reserved); Building Area Extraction; Water Area Extraction;
Language:Python 9fliphilipp / icesat2geemap
Interactively visualize and contextualize high-resolution spaceborne LiDAR data from NASA's ICESat-2 mission, using the OpenAltimetry API along with the Google Earth Engine Python API and the python package geemap for mapping.
Language:Jupyter Notebook 7Serverless Landsat map tiles from mosaics of Cloud-Optimized GeoTIFFs
Language:Jupyter Notebook 7The Short-term Forest Change Tool (STFC) is a Google Earth Engine script created by the Spring 2020 Costa Rica and Panama Ecological Forecasting team. The main scope of the software is to display changes in vegetation of forested areas and identify regions of possible deforestation.
Language:JavaScript 6The Wetland Extent Tool (WET) was developed by the 2019 Spring JPL Great Lakes Water Resources team for wetland mapping in Minnesota using Sentinel-1 C-SAR, Landsat 8 OLI, and a LiDAR-derived Topographic Wetness Index (TWI) in Google Earth Engine.
ryankemmer / Landsat-TimeLapser
An open-source web application for creating time-lapses with Landsat 8 Satellite Imagery powered by Google Earth Engine. 🛰️
Language:JavaScript 6brazil-data-cube / bdc-collectors
Flask extension for Brazil Data Cube to collect satellite imagery from multiple providers.
Language:Python 5ggonzr / region_grow
Creates a polygon using a set of points from a region of interest by grouping pixels whose spectral reflectance is similar. The polygons are created using a satellite image in GeoTIFF format. In this project several algorithms are implemented to build this figure. Among them are: Selection by similarity threshold (%), Euclidean distance and selection by confidence interval. The generated polygon is exported in ESRI Shapefile format
Language:Python 5ocsmit / rindcalc
A Python module for remote sensing index calculations and processing. Mostly used for my experimentation with new concepts and techniques.
Language:Python 5arnabsaha7 / SmartGeoMapping
Random Forest classification tool using LANDSAT 8 for location-based risk analysis, featuring Google Earth Engine and interactive visualizations of Land Cover.
Language:Jupyter Notebook 4Create mosaicJSON for Landsat imagery
Language:Python 4The Supervised Land Cover Classification (SLaCC) tool is a Google Earth Engine script created by the Summer 2019 Southern Maine Health and Air Quality Team. It uses NASA Earth observations, the National Land Cover Database, land cover classification training data, and a shapefile of Cumberland County, Maine, USA. The goal of the project was to evaluate land cover and tick habitat suitability in southern Maine. The SLaCC script occurs in two parts. Part 1 of the script allows users to create a supervised land cover map over a region using a Classification And Regression Tree (CART) model. Part 2 of the script allows users to create a map that displays the "edges" of chosen land covers.
Language:JavaScript 4JoshuaBillson / LandsatExplorer.jl
A pure Julia package for querying and downloading Landsat data.
Language:Julia 3
