rasterio
awesome-spectral-indices
| rasterio | awesome-spectral-indices | |
|---|---|---|
| 6 | 4 | |
| 2,138 | 761 | |
| 0.7% | 2.2% | |
| 8.6 | 5.9 | |
| 12 days ago | 6 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rasterio
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Building a Dynamic Tile Server Using Cloud Optimized GeoTIFF(COG) with TiTiler
TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.
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Python: How to save NumPy 2d-array to an asc-file so that it can be opened in ArcGIS Pro?
You're missing transform headers etc, you can write it out with GDAL but rasterio is generally easier to use than the Python GDAL bindings, here's an example.
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How to convert (geo?)tifs into something I can query with geocoordinates (PostgreSQL?) ?
Out of curiosity, what's stopping you from keeping the data in GTiff and just sampling from them with coordinates? Lots of free software lets you do that easily (e.g. QGIS, rasterio if you use Python etc). Am I not seeing something here?
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Library Writing Realizations
> X. People will not read the docs.
> Docs are almost useless. Nobody reads them.
> They'll read a one page quick start, and then they want to just start digging in writing code.
> Keep the intros very minimal and very focused on getting things working.
I, for one, read the docs. May fortune smile on those who put effort into https://rasterio.readthedocs.io/
They are terse, but enough breadcrumbs to fumble my way to a working input for gdal_proximity.py
Possibly I'm not a representative sample, but one of my mantras at the office is "If you ain't doc'in', you ain't rockin'."
(Though we are only doing ransom note wiki pages, not library documentation.)
If the package doesn't have shiny Sphynx docs, at least afford us a few cryptic utterances in the source code.
- How do I open .vrt file to see content of the file in python?
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Better alternative to gdal_translate [cross posted with r/QGIS]
Looks like rio (Rasterio CLI) also uses gdal libraries. https://github.com/rasterio/rasterio/blob/master/rasterio/rio/clip.py
awesome-spectral-indices
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Any tips or resources for vegetation analysis using 8 band imagery compared to 4 band?
You might find this github repo useful: https://github.com/awesome-spectral-indices/awesome-spectral-indices
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Best indices to use for detecting shallow water
Have you tried the augmented NDWI? That link has a ton of other indices too (scroll down for water).
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Vegetation Indices to determine the change in vegetation area using aerial images
How do you define vegetation cover to begin with? You'll need some kind of definition that uses RGB only, if you want to use these images. Have a look at this listto see if there's a vegetation index that uses RGB only.
- Spectral Indices for Remote Sensing Applications
What are some alternatives?
titiler - Build your own Raster dynamic map tile services
biodivMapR - biodivMapR: an R package for α- and β-diversity mapping using remotely-sensed images
earthpy - A package built to support working with spatial data using open source python
geemap - A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
BlenderGIS - Blender addons to make the bridge between Blender and geographic data
wxee - A Python interface between Earth Engine and xarray for processing time series data
detectree - Tree detection from aerial imagery in Python
flood-sim - This repo simulates water flooding.
linqit - Extend python lists operations using .NET's LINQ syntax for clean and fast coding.
geoserver-rest - Python library for management for geospatial data in GeoServer.
TouchTerrain_for_CAGEO - Touch Terrain: A python app to create 3D printable terrain models (STL/OBJ) from only elevation data (via Google Earth Engine) or from a local geotiff. Has been used for CNC terrain models. Runs as a web app (http://touchterrain.org), as .py file (standalone.py) or as jupyter notebook. Docker image: https://github.com/ChHarding/TouchTerrain_jupyter_docker