Building Height Prediction with Instance Segmentation

• URL: http://arxiv.org/abs/2212.09277v1
• Date: Mon, 19 Dec 2022 07:12:49 GMT
• Title: Building Height Prediction with Instance Segmentation
• Authors: Furkan Burak Bagci, Ahmet Alp Kindriroglu, Metehan Yalcin, Ufuk Uyan, Mahiye Uluyagmur Ozturk
• Abstract summary: We present an instance segmentation-based building height extraction method to predict building masks from a single RGB satellite image. We used satellite images with building height annotations of certain cities along with an open-source satellite dataset with the transfer learning approach. We reached, the bounding box mAP 59, the mask mAP 52.6, and the average accuracy value of 70% for buildings belonging to each height class in our test set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting building heights from satellite images is an active research area used in many fields such as telecommunications, city planning, etc. Many studies utilize DSM (Digital Surface Models) generated with lidars or stereo images for this purpose. Predicting the height of the buildings using only RGB images is challenging due to the insufficient amount of data, low data quality, variations of building types, different angles of light and shadow, etc. In this study, we present an instance segmentation-based building height extraction method to predict building masks with their respective heights from a single RGB satellite image. We used satellite images with building height annotations of certain cities along with an open-source satellite dataset with the transfer learning approach. We reached, the bounding box mAP 59, the mask mAP 52.6, and the average accuracy value of 70% for buildings belonging to each height class in our test set.

Related papers

• Building Height Estimation Using Shadow Length in Satellite Imagery [2.7548979811246292]
  Estimating building height from satellite imagery poses significant challenges, especially when monocular images are employed. We proposed a novel method that first localized a building and its shadow in the given satellite image. We evaluated our method on 42 different cities and the results showed that the proposed framework surpasses the state-of-the-art methods with a suitable margin.
  arXiv  Detail & Related papers  (2024-11-14T13:06:18Z)
• SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection [79.23689506129733]
  We establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created.
  arXiv  Detail & Related papers  (2024-03-11T09:20:40Z)
• DiffusionSat: A Generative Foundation Model for Satellite Imagery [63.2807119794691]
  We present DiffusionSat, to date the largest generative foundation model trained on a collection of publicly available large, high-resolution remote sensing datasets. Our method produces realistic samples and can be used to solve multiple generative tasks including temporal generation, superresolution given multi-spectral inputs and in-painting.
  arXiv  Detail & Related papers  (2023-12-06T16:53:17Z)
• Building Floorspace in China: A Dataset and Learning Pipeline [0.32228025627337864]
  This paper provides a first milestone in measuring the floorspace of buildings in 40 major Chinese cities. We use Sentinel-1 and -2 satellite images as our main data source. We provide a detailed description of our data, algorithms, and evaluations.
  arXiv  Detail & Related papers  (2023-03-03T21:45:36Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• Buildings Classification using Very High Resolution Satellite Imagery [0.769672852567215]
  We focus on buildings damage assessment (BDA) and buildings type classification (BTC) of residential and non-residential buildings. We propose a 2-stage deep learning-based approach, where first, buildings' footprints are extracted using a semantic segmentation model. We validate the proposed approach on two applications showing excellent accuracy and F1-score metrics.
  arXiv  Detail & Related papers  (2021-11-29T16:07:04Z)
• Wide-Depth-Range 6D Object Pose Estimation in Space [124.94794113264194]
  6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric scattering, allowing objects to be visible from a great distance. We propose a single-stage hierarchical end-to-end trainable network that is more robust to scale variations.
  arXiv  Detail & Related papers  (2021-04-01T08:39:26Z)
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges [124.48654341780431]
  We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
  arXiv  Detail & Related papers  (2021-02-24T11:20:55Z)
• Holistic Multi-View Building Analysis in the Wild with Projection Pooling [18.93067906200084]
  We address six different classification tasks related to fine-grained building attributes. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. We introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings.
  arXiv  Detail & Related papers  (2020-08-23T13:49:22Z)
• Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds [39.36437891978871]
  We present a reliable and effective approach for building model reconstruction from the point clouds generated from satellite images. Specifically, a deep-learning approach is adopted to distinguish the shape of building roofs in complex and yet noisy scenes. As the first effort to address the public need of large scale city model generation, the development is deployed as open source software.
  arXiv  Detail & Related papers  (2020-05-19T05:38:06Z)
• Counting dense objects in remote sensing images [52.182698295053264]
  Estimating number of interested objects from a given image is a challenging yet important task. In this paper, we are interested in counting dense objects from remote sensing images. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image.
  arXiv  Detail & Related papers  (2020-02-14T09:13:54Z)

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.