Classification of residential and non-residential buildings based on satellite data using deep learning

• URL: http://arxiv.org/abs/2411.06879v1
• Date: Mon, 11 Nov 2024 11:23:43 GMT
• Title: Classification of residential and non-residential buildings based on satellite data using deep learning
• Authors: Jai G Singla,
• Abstract summary: In this paper, we are proposing a novel deep learning approach that combines high-resolution satellite data and vector data to achieve high-performance building classification. Experimental results on a large-scale dataset demonstrate the effectiveness of our model, achieving an impressive overall F1 -score of 0.9936.
• Score: 0.0
• License:
• Abstract: Accurate classification of buildings into residential and non-residential categories is crucial for urban planning, infrastructure development, population estimation and resource allocation. It is a complex job to carry out automatic classification of residential and nonresidential buildings manually using satellite data. In this paper, we are proposing a novel deep learning approach that combines high-resolution satellite data (50 cm resolution Image + 1m grid interval DEM) and vector data to achieve high-performance building classification. Our architecture leverages LeakyReLU and ReLU activations to capture nonlinearities in the data and employs feature-engineering techniques to eliminate highly correlated features, resulting in improved computational efficiency. Experimental results on a large-scale dataset demonstrate the effectiveness of our model, achieving an impressive overall F1 -score of 0.9936. The proposed approach offers a scalable and accurate solution for building classification, enabling informed decision-making in urban planning and resource allocation. This research contributes to the field of urban analysis by providing a valuable tool for understanding the built environment and optimizing resource utilization.

Related papers

• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Extracting the U.S. building types from OpenStreetMap data [0.16060719742433224]
  This work creates a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We propose and utilize an unsupervised machine learning method to classify building types based on building footprints and available OpenStreetMap information. The validation shows a high precision for non-residential building classification and a high recall for residential buildings.
  arXiv  Detail & Related papers  (2024-09-09T15:05:27Z)
• Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning [18.432786227782803]
  We propose a geometry-aware semi-supervised framework for fine-grained building function recognition. We use geometric relationships among multi-source data to enhance pseudo-label accuracy in semi-supervised learning. Our proposed framework exhibits superior performance in fine-grained functional recognition of buildings.
  arXiv  Detail & Related papers  (2024-08-18T12:48:48Z)
• UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images [7.435848987082052]
  We propose a deep network approach to fine-grained classification of urban buildings using open-access satellite images. A new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance. Experiments on Hong Kong data set with 11 fine building types revealed promising classification results with a mean Top-1 accuracy of 60.45%.
  arXiv  Detail & Related papers  (2024-03-04T15:40:31Z)
• Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery [13.544826927121992]
  Multi-task Building Refiner (MT-BR) is an adaptable neural network tailored for simultaneous extraction of building details from satellite imagery. For large-scale applications, we devise a novel spatial sampling scheme that strategically selects limited but representative image samples. MT-BR consistently outperforms other state-of-the-art methods in extracting building details across various metrics.
  arXiv  Detail & Related papers  (2023-10-29T04:43:30Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation [59.6553058160943]
  We propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OpenStreetMap data. The proposed method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data.
  arXiv  Detail & Related papers  (2023-07-05T18:16:30Z)
• Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
  We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery. Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
  arXiv  Detail & Related papers  (2023-01-04T05:19:33Z)
• 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)
• Landmark Policy Optimization for Object Navigation Task [77.34726150561087]
  This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments. Recent works have shown significant achievements both in the end-to-end Reinforcement Learning approach and modular systems, but need a big step forward to be robust and optimal. We propose a hierarchical method that incorporates standard task formulation and additional area knowledge as landmarks, with a way to extract these landmarks.
  arXiv  Detail & Related papers  (2021-09-17T12:28:46Z)
• Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search [50.40004966087121]
  We introduce a new reinforcement learning based neural architecture search (NAS) methodology for generative adversarial network (GAN) architecture search. The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling. We exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies.
  arXiv  Detail & Related papers  (2020-07-17T18:29:17Z)

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.