Pantropical Shifting Cultivation Map — Model Weights and Example Data

This deposit contains the trained model weights and example data for the paper:

A pantropical map of shifting cultivation at 1 km resolution using deep learning and PlanetScope imagery [Author list, Journal, Year]

Contents

bestF1.pkl — Trained EfficientNet-B1 classifier weights (PyTorch). The model was trained on 4-band (R, G, B, NIR) PlanetScope NICFI basemap patches at 234×234 pixels to classify five tropical land-use types: high tree-cover woodland, shifting cultivation, conventional agriculture, mixed woody plantation and others. Weights correspond to the epoch with the highest validation macro-F1 score.

samples_example.zip — A set of example PlanetScope training patches in .npy format (channel-first, 4×234×234, float32) illustrating the input data format expected by the model.

sample_csv_demo.csv — Example label CSV showing the required column format for training: path (file path to patch), label (land-use class string), useCase (train/test), augProb (per-sampl