A Unified Multispectral Feature Framework for Independent Recognition of Floods, Burned Areas, and Landslides

Multispectral disaster recognition draws on common optical measurements, but differences in feature construction, target definitions and evaluation design complicate comparisons across tasks. We present a unified feature framework that applies the same six-band interface, 78-dimensional spectral–spatial descriptor and training-side learning procedure to independently fitted flood, burned area and landslide experts. Spectral summaries, local variability and gradients describe each window; paired date and source auxiliary inputs provide task-specific extensions. Evaluation covered 88 flood patches from 11 events, 656 burned area windows in 23 source/overlap groups, and a prespecified 100-patch subset of the official Landslide4Sense test partition. The shared representation achieved F1 values of 0.8504, 0.8485 and 0.8169, respectively, under training-side classifier selection. The burned area temporal extension increased F1 to 0.8997, with gains over matched support post-event descriptors under both inner-selected and fixed Random Forest evaluation. The landslide auxiliary extension produced a larger gain under classifier selection than under fixed Random Forest, identifying an interaction between representation and classifier. Training label controls and negative class metrics distinguished learned discrimination from positive class prevalence. The framework provides a reproducible basis for testing shared optical descriptors and task-specific information under the distinct evaluation requirements of each task.