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spatialcvR - Spatial Cross-Validation for Machine Learning

Spatial cross-validation and model evaluation for geospatial machine learning applications. Addresses spatial dependence in observations by implementing spatial block, buffered, and clustering cross-validation methods. Includes spatial leakage detection, model performance metrics, and spatial residual diagnostics for assessing model generalization across geographic space. Methods based on Brenning (2012) <doi:10.1016/j.cageo.2012.02.001>, Pohjankukka et al. (2017) <doi:10.1016/j.isprsjprs.2017.07.001>, and Roberts et al. (2017) <doi:10.1111/ecog.02881>.

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4.78 score 1 stars 64 downloads

GeoIndexR - Computation of Spectral and Geospatial Indices from Multispectral Raster Data

A unified, fast, and extensible framework for calculating spectral and geospatial indices from multispectral raster datasets (e.g., NDVI - Normalized Difference Vegetation Index, NDWI - Normalized Difference Water Index, MNDWI - Modified Normalized Difference Water Index, NDBI - Normalized Difference Built-up Index, SAVI - Soil Adjusted Vegetation Index, EVI - Enhanced Vegetation Index, GNDVI - Green Normalized Difference Vegetation Index, NDMI - Normalized Difference Moisture Index, BSI - Bare Soil Index). Designed around 'terra' 'SpatRaster' objects, it supports multi-band rasters, automatic band resolution, sensor presets (Sentinel-2, Landsat-8/9), customizable index parameters, vectorized computations, and comprehensive validation. Methods based on Rouse et al. (1973) , McFeeters (1996) <doi:10.1080/01431169608948714>, Xu (2006) <doi:10.1080/01431160600589179>, Zha et al. (2003) <doi:10.1016/S0034-4257(03)00087-7>, Huete (1988) <doi:10.1016/0034-4257(88)90106-X>, Gitelson et al. (1996) <doi:10.1016/S0034-4257(96)00072-7>, Wilson and Sader (2002) <doi:10.1016/S0034-4257(01)00318-2>, and Rikimaru et al. (2002).

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4.60 score