Georeferenced Rasters and Statistics of Nightlights from NASA Black Marble
Georeferenced Rasters and Statistics of Nighttime Lights from NASA Black Marble
Collection of python tools for running zonal stats on Google Earth Engine layers
District-wise nighttime lights database for India (641 districts, 2012-2024) using VIIRS satellite data.
QGIS Plugin to perform Area Weighted Average analysis.
Extract remote sensing metrics for spatial health analysis 🛰️, integrating vector-borne disease data. Easily compute areal and zonal statistics for infectious disease modelling in spatial epidemiology 🦠🏥
This repo demonstrates how to easily overlay community polygons on forest cover data and make a beautiful map using R and ggplot2
POC of using AWS Lambda + Rasterio for raster calculations on S3
Extractor allows for the extraction of data from raster images based on point, line, and polygon vectors and the application of zonal statistics. Its usefulness lies in its ability to work with a range of raster sources, making it ideal for time series analysis and landscape studies.
A set of python scripts for spatially explicit accuracy assessments of binary, gridded geospatial data, e.g., for human settlement data mapping built-up (1) and not built-up (0) areas.
Test with Python module rasterstats.
Analyzing tree canopy equity in St. Louis City using NLCD 2023 raster data + ACS 5-year estimates
A Collection of Python Script to perform analysis in QGIS Python Environment
Winter wheat NDVI and NIRv for all 96 Bavarian districts, March-June 2017-2024, from 1.13 TB of DLR Sentinel-2 monthly composites masked with DLR 10 m crop maps. GPU pipeline (CuPy, exactextract) on Colab: 1.64 billion wheat pixels in 5.5 h. Over 151,793 OCO-2 footprints, SIF tracks NIRv (r=0.54) better than NDVI (r=0.42).
Tree species classification using Bengaluru municipal data and 3m planetscope imagery
associated with video to demonstrate zonal statistics algorithm
Interactive raster statistics / zonal statistics through rasterstats and ipywidgets: a solid waste use case.
Raster and vector geospatial analysis in Python: mean SRTM elevation per NUTS-1 region, raster to point grids, reprojection, and a finished map of Manhattan parks, bike routes and 2050s flood zones. Four Colab notebooks, all data from open sources, written while following Milan Janosov's LinkedIn Learning courses.
Streaming Monte Carlo zonal-diff over baseline and scenario rasters for nature restoration scenario assessment.
To associate your repository with the zonal-statistics topic, visit your repo's landing page and select "manage topics."