landsat-8

There are 88 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:Python9102855147

  • kvos/CoastSat

    Global shoreline mapping tool from satellite imagery

    Language:Jupyter Notebook72427411257

  • pylandtemp/pylandtemp

    Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.

    Language:Python17471030
  • deck.gl-raster

    kylebarron/deck.gl-raster

    deck.gl layers and WebGL modules for client-side satellite imagery analysis

    Language:JavaScript87114310

  • 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:MATLAB652116

  • MarcYin/SIAC

    A sensor invariant Atmospheric Correction (SIAC)

    Language:C6452717

  • olivierhagolle/Start_maja

    To process a Sentinel-2 time series with MAJA cloud detection and atmospheric correction processor

    Language:Python51173015
  • 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:Python44421

  • RAJohansen/waterquality

    Package designed to detect and quantify water quality and cyanobacterial harmful algal bloom (CHABs) from remotely sensed imagery

    Language:R438188
  • landsat8.earth

    kylebarron/landsat8.earth

    2D/3D WebGL Landsat 8 satellite image analysis

    Language:JavaScript405464

  • multiply-org/atmospheric_correction

    Sentinel 2 and Landsat 8 Atmospheric correction

    Language:C3571311

  • eupassarinho/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:JavaScript31106

  • amanbasu/wildfire-detection

    Using Vision Transformers for enhanced wildfire detection in satellite images

    Language:Python24126

  • GenericMappingTools/RemoteS.jl

    Remote sensing data processing

    Language:Julia195284

  • calekochenour/gee-vegetation-change

    Reproducible remote sensing analysis using Google Earth Engine (GEE) to identify vegetation change in Columbia.

    Language:Jupyter Notebook18206

  • AlexeyPechnikov/satellite-spectrogram

    Compare Spectrograms of Hyperspectral and Multispectral Satellite Missions

    Language:Jupyter Notebook15105

  • kylebarron/landsat-mosaic-latest

    Auto-updating global Landsat 8 mosaic of Cloud-Optimized GeoTIFFs from SNS notifications

    Language:Python155132

  • kunzhan/HR-cloud-Net

    High-Resolution Cloud Detection Network

    Language:Python1410

  • 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.

    13614

  • 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 Notebook12122

  • stevinc/EGNNA_WND

    Pytorch code for estimating the presence of the West Nile Disease employing Graph Neural network

    Language:Python12101

  • NASA-DEVELOP/WET2.0

    The 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).

    11513

  • saraivaufc/instance-segmentation-maskrcnn

    Instance segmentation of center pivot irrigation system in Brazil using Landsat images and Convolutional Neural Network

    Language:Jupyter Notebook11112

  • ElementMo/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:Python9105

  • fliphilipp/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 Notebook7103

  • kylebarron/landsat-mosaic-tiler

    Serverless Landsat map tiles from mosaics of Cloud-Optimized GeoTIFFs

    Language:Jupyter Notebook733

  • NASA-DEVELOP/STFC

    The 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:JavaScript7502

  • NASA-DEVELOP/WET

    The 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.

    6511

  • ryankemmer/Landsat-TimeLapser

    An open-source web application for creating time-lapses with Landsat 8 Satellite Imagery powered by Google Earth Engine. 🛰️

    Language:JavaScript6201

  • brazil-data-cube/bdc-collectors

    Flask extension for Brazil Data Cube to collect satellite imagery from multiple providers.

    Language:Python54403

  • ggonzr/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:Python5103

  • ocsmit/rindcalc

    A Python module for remote sensing index calculations and processing. Mostly used for my experimentation with new concepts and techniques.

    Language:Python5133

  • kylebarron/landsat-cogeo-mosaic

    Create mosaicJSON for Landsat imagery

    Language:Python4417

  • NASA-DEVELOP/SLaCC

    The 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:JavaScript4505

  • arnabsaha7/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 Notebook3100

  • JoshuaBillson/LandsatExplorer.jl

    A pure Julia package for querying and downloading Landsat data.

    Language:Julia3140