Building Change Detection using Multi-Temporal Airborne LiDAR Data

• URL: http://arxiv.org/abs/2204.12535v1
• Date: Tue, 26 Apr 2022 18:43:29 GMT
• Title: Building Change Detection using Multi-Temporal Airborne LiDAR Data
• Abstract summary: 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation without losing the necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: 'newly built', 'demolished', 'taller' and 'shorter'. The detected changes are visualized in one map for better interpretation.

Related papers

• TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decoder [66.22997415145467]
  This paper presents a joint completion and detection framework that improves the detection feature in sparse areas.<n> Specifically, we propose TransBridge, a novel transformer-based up-sampling block that fuses the features from the detection and completion networks.<n>The results show that our framework consistently improves end-to-end 3D object detection, with the mean average precision (mAP) ranging from 0.7 to 1.5 across multiple methods.
  arXiv  Detail & Related papers  (2025-12-12T00:08:03Z)
• SceneEdited: A City-Scale Benchmark for 3D HD Map Updating via Image-Guided Change Detection [20.280721190285025]
  SceneEdited is the first city-scale dataset explicitly designed to support research on HD map maintenance through 3D point cloud updating.<n>SceneEdited contains over 800 up-to-date scenes covering 73 km of driving and approximate 3 $textkm2$ of urban area.<n>Each scene includes calibrated RGB images, LiDAR scans, and detailed change masks for training and evaluation.
  arXiv  Detail & Related papers  (2025-11-19T06:10:55Z)
• ME-CPT: Multi-Task Enhanced Cross-Temporal Point Transformer for Urban 3D Change Detection [10.15947374827254]
  By utilizing multi-temporal ALS point clouds, semantic changes in urban area can be captured. Existing 3D change detection methods struggle to efficiently extract multi-class semantic information and change features. We propose the Multi-task Enhanced Cross-temporal Point Transformer (ME-CPT) network.
  arXiv  Detail & Related papers  (2025-01-23T13:07:41Z)
• Research on self-cross transformer model of point cloud change detecter [2.3838507844983248]
  In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches. In this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer.
  arXiv  Detail & Related papers  (2023-09-14T05:54:54Z)
• DC3DCD: unsupervised learning for multiclass 3D point cloud change detection [0.0]
  We propose an unsupervised method, called Deep 3D Change Detection (DC3DCD), to detect and categorize multiclass changes point level. Our method builds upon the DeepCluster approach, originally designed for image classification, to handle complex raw 3D PCs.
  arXiv  Detail & Related papers  (2023-05-09T13:13:53Z)
• Optimal Transport for Change Detection on LiDAR Point Clouds [16.552050876277242]
  Unsupervised change detection between airborne LiDAR data points is difficult due to unmatching spatial support and noise from the acquisition system. We propose an unsupervised approach based on the computation of the transport of 3D LiDAR points over two temporal supports. Our method allows for unsupervised multi-class classification and outperforms the previous state-of-the-art unsupervised approaches by a significant margin.
  arXiv  Detail & Related papers  (2023-02-14T13:08:07Z)
• LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations [55.45435708426761]
  LidarAugment is a search-based data augmentation strategy for 3D object detection. We show LidarAugment can be customized for different model architectures. It consistently improves convolution-based UPillars/StarNet/RSN and transformer-based SWFormer.
  arXiv  Detail & Related papers  (2022-10-24T18:00:04Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
  In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
  arXiv  Detail & Related papers  (2021-12-13T02:12:02Z)
• Deep few-shot learning for bi-temporal building change detection [0.0]
  A new deep few-shot learning method is proposed for building change detection using Monte Carlo dropout and remote sensing observations. The setup is based on a small dataset, including bitemporal optical images labeled for building change detection.
  arXiv  Detail & Related papers  (2021-08-25T14:38:21Z)
• City-scale Scene Change Detection using Point Clouds [71.73273007900717]
  We propose a method for detecting structural changes in a city using images captured from mounted cameras over two different times. A direct comparison of the two point clouds for change detection is not ideal due to inaccurate geo-location information. To circumvent this problem, we propose a deep learning-based non-rigid registration on the point clouds. Experiments show that our method is able to detect scene changes effectively, even in the presence of viewpoint and illumination differences.
  arXiv  Detail & Related papers  (2021-03-26T08:04:13Z)
• Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images [15.466320414614971]
  This paper proposes a fused feature pyramid network to detect changes in buildings in urban environments. It uses both color and depth data for the 3D verification of existing buildings 2D footprints from oblique images. We demonstrate that the proposed method can successfully detect all changed buildings.
  arXiv  Detail & Related papers  (2020-11-26T10:51:36Z)
• Improving 3D Object Detection through Progressive Population Based Augmentation [91.56261177665762]
  We present the first attempt to automate the design of data augmentation policies for 3D object detection. We introduce the Progressive Population Based Augmentation (PPBA) algorithm, which learns to optimize augmentation strategies by narrowing down the search space and adopting the best parameters discovered in previous iterations. We find that PPBA may be up to 10x more data efficient than baseline 3D detection models without augmentation, highlighting that 3D detection models may achieve competitive accuracy with far fewer labeled examples.
  arXiv  Detail & Related papers  (2020-04-02T05:57:02Z)
• D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [51.04841465193678]
  We leverage a 3D fully convolutional network for 3D point clouds. We propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point. Our method achieves state-of-the-art results in both indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2020-03-06T12:51:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.