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Change detection is one of the most active research areas in Remote Sensing(RS). Most of the recently developed change detection methods are based on deeplearning (DL) algorithms. This kind of algorithms is generally focused ongenerating two-dimensional (2D) change maps, thus only identifying planimetricchanges in land use/land cover (LULC) and not considering nor returning anyinformation on the corresponding elevation changes. Our work goes one stepfurther, proposing two novel networks, able to solve simultaneously the 2D and3D CD tasks, and the 3DCD dataset, a novel and freely available datasetprecisely designed for this multitask. Particularly, the aim of this work is tolay the foundations for the development of DL algorithms able to automaticallyinfer an elevation (3D) CD map -- together with a standard 2D CD map --,starting only from a pair of bitemporal optical images. The proposedarchitectures, to perform the task described before, consist of atransformer-based network, the MultiTask Bitemporal Images Transformer (MTBIT),and a deep convolutional network, the Siamese ResUNet (SUNet). Particularly,MTBIT is a transformer-based architecture, based on a semantic tokenizer. SUNetinstead combines, in a siamese encoder, skip connections and residual layers tolearn rich features, capable to solve efficiently the proposed task. Thesemodels are, thus, able to obtain 3D CD maps from two optical images taken atdifferent time instants, without the need to rely directly on elevation dataduring the inference step. Encouraging results, obtained on the novel 3DCDdataset, are shown. The code and the 3DCD dataset are available athttps://sites.google.com/uniroma1.it/3dchangedetection/home-page.

Inferring 3D change detection from bitemporal optical images