# Inferring 3D change detection from bitemporal optical images arXiv: https://arxiv.org/abs/2205.15903 ยท HTML: https://mlsanity.com/paper/2205.15903/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 56 references that are in the map. - First posted on arXiv 31 May 2022. ## Builds on (most cited first, 19 of 19) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016) - [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020) - [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018) - [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021) - [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022) - [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017) - [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018) - [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022) - [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021) - [IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network](https://mlsanity.com/paper/1802.10249/) (2018) - [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022) - [Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network](https://mlsanity.com/paper/1912.08628/) (2019) - [Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions](https://mlsanity.com/paper/2103.00188/) (2021) - [Self-supervised pre-training enhances change detection in Sentinel-2 imagery](https://mlsanity.com/paper/2101.08122/) (2021) - [A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse](https://mlsanity.com/paper/2108.02800/) (2021) ## Built on by (most cited first, 10 of 10) - [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024) - [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023) - [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022) - [Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration](https://mlsanity.com/paper/2406.17458/) (2024) - [Transformer-based Multimodal Change Detection with Multitask Consistency Constraints](https://mlsanity.com/paper/2310.09276/) (2023) - [Multi-temporal Calving Front Segmentation](https://mlsanity.com/paper/2512.11560/) (2025) - [Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline](https://mlsanity.com/paper/2603.19077/) (2026) - [DPG-CD: Depth-Prior-Guided Cross-Modal Joint 2D-3D Change Detection](https://mlsanity.com/paper/2605.07151/) (2026) - [JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering](https://mlsanity.com/paper/2606.31745/) (2026) - [Uncertainty-aware tree height change regression](https://mlsanity.com/paper/2607.00638/) (2026) ## Abstract (arXiv) 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 transformer-based 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.