The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1349–1354, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1349-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1349–1354, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1349-2022
31 May 2022

# 3DCD: A NEW DATASET FOR 2D AND 3D CHANGE DETECTION USING DEEP LEARNING TECHNIQUES

V. Coletta1, V. Marsocci2, and R. Ravanelli1 V. Coletta et al.
• 1Geodesy and Geomatics Division (DICEA) - Sapienza University of Rome, Rome, Italy
• 2Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) - Sapienza University of Rome, Rome, Italy

Keywords: 3D change detection, Dataset, 2D change detection, Remote Sensing, Deep learning

Abstract. Change detection is one of the main topics in Earth Observation, due to its wide range of applications, varying from urban development monitoring to natural disaster management. Most of the recently developed change detection methodologies rely on the use of deep learning algorithms. These kinds of algorithms are generally focused on generating two-dimensional (2D) change maps, thus they are only able to detect horizontal changes in land use/land cover, not considering nor returning any information on the corresponding elevation changes. Our work proposes a step forward, creating and sharing a dataset where two optical images acquired in different epochs are provided together with both the related 2D change maps containing land use/land cover variations and the three-dimensional (3D) maps containing elevation changes. Particularly, our aim is to provide a dataset useful to address and possibly solve the change detection task in 3D. Indeed, the proposed dataset, on the one hand, can empower a further development of 2D change detection algorithms, and, on the other hand, can allow to develop algorithms able to provide 3D change detection maps from two optical images captured in different epochs, without the need to rely directly on elevation data as input. The proposed dataset is publicly available at the following link: https://bit.ly/3wDdo41.