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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.

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 deep learning (DL) algorithms. This kind of algorithms is generally focused on generating two-dimensional (2D) change maps, thus only identifying planimetric changes in land use/land cover (LULC) and not considering nor returning any information on the corresponding elevation changes. Our work goes one step further, proposing two novel networks, able to solve simultaneously the 2D and 3D CD tasks, and the 3DCD dataset, a novel and freely available dataset precisely designed for this multitask. Particularly, the aim of this work is to lay the foundations for the development of DL algorithms able to automatically infer an elevation (3D) CD map -together with a standard 2D CD map -, starting only from a pair of bitemporal optical images. The proposed architectures, to perform the task described before, consist of a transformer-based network, the MultiTask Bitemporal Images Transformer (MTBIT), and a deep convolutional network, the Siamese ResUNet (SUNet). Particularly, MTBIT is a transformerbased architecture, based on a semantic tokenizer. SUNet instead combines, in a siamese encoder, skip connections and residual layers to learn rich features, capable to solve efficiently the proposed task. These models are, thus, able to obtain 3D CD maps from two optical images taken at different time instants, without the need to rely directly on elevation data during the inference step. Encouraging results, obtained on the novel 3DCD dataset, are shown. The code and the 3DCD dataset are available at https://sites.google.com/uniroma1. it/3dchangedetection/home-page.

<p><strong>Keywords</strong>: Urban sustainability, Earth observation, 3D change detection, Deep Learning, Dataset</p> <p>Nowadays, remote sensing products can provide useful and consistent information about urban areas and their morphological structures with different spatial and temporal resolutions, making it possible to perform long term spatiotemporal analyses of the historic development of the cities and in this way to monitor the evolution of their urbanization patterns, a goal strictly related to the United Nations (UN) Sustainable Development Goals (SDGs) concerning the sustainability of the cities (SDG 11 - Sustainable Cities and Communities).</p> <p>In this context, Change Detection (CD) algorithms estimate the changes occurred at ground level and are employed in a wide range of applications, including the identification of urban changes. Most of the recently developed CD methodologies rely on deep learning architectures. Nevertheless, the CD algorithms currently available are mainly focused on generating two-dimensional (2D) change maps, where the planimetric extent of the areas affected by changes is identified without providing any information on the corresponding elevation (3D) variations. These algorithms can thus only identify planimetric changes such as appearing/disappearing buildings/trees, shrinking/expanding structures and are not able to satisfy the requirements of applications which need to detect and, most of all, to quantify the elevation variations occurred in the area of interest (AOI), such as the estimation of volumetric changes in urban areas.</p> <p>It is therefore essential to develop CD algorithms capable of automatically generating an elevation (3D) CD map (a map containing the quantitative changes in elevation for the AOI) together with a standard 2D CD map, from the smallest possible amount of information. In this contribution, we will present the MultiTask Bitemporal Images Transformer (MTBIT) [1], a recently developed network, belonging to the family of vision Transformers and based on a semantic tokenizer, explicitly designed to solve the 2D and 3D CD tasks simultaneously from bitemporal optical images, and thus without the need to rely directly on elevation data during the inference phase. </p> <p>The MTBIT performances were evaluated in the urban area of Valladolid on the modified version of the 3DCD dataset [2], comparing this architecture with other networks designed to solve the 2D CD task. In particular, MTBIT reaches a metric accuracy equal to 6.46 m – the best performance among the compared architectures – with a limited number of parameters (13,1 M) [1].</p> <p>The code and the 3DCD dataset are available at https://sites.google.com/uniroma1.it/3dchangedetection/home-page.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., and Crespi, M.: Inferring 3D change detection from bitemporal optical images. <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. 2023 - in press.</p> <p>[2] Coletta, V., Marsocci, V., and Ravanelli, R.: 3DCD: A NEW DATASET FOR 2D AND 3D CHANGE DETECTION USING DEEP LEARNING TECHNIQUES,<em> The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</em>, XLIII-B3-2022, 1349–1354, 2022.</p>