1-km pantropical shifting cultivation map

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-sample augmentation probability for weakly supervised learning), id (site identifier for spatial cross-validation splitting). Related resources Code: https://github.com/ywant/shifting_cultivation_1km Model architecture: EfficientNet-B1 with ImageNet pre-trained weights, 4-channel input, 4-layer dense classification head Input imagery: NICFI PlanetScope basemaps accessed via the Planet NICFI programme Map outputs: to be prepared Reproduce the results Download bestF1.pkl, place it at saved_models/bestF1.pkl, and follow the instructions in the GitHub repository README to run inference or reproduce the accuracy assessment.