Estimate the building height at a 10-meter resolution based on Sentinel data

• URL: http://arxiv.org/abs/2405.00989v1
• Date: Thu, 2 May 2024 03:53:59 GMT
• Title: Estimate the building height at a 10-meter resolution based on Sentinel data
• Authors: Xin Yan,
• Abstract summary: This study established a set of spatial-spectral-temporal feature databases. It combined SAR data provided by Sentinel-1, optical data provided by Sentinel-2, and shape data provided by building footprints. The statistical indicators on the time scale are extracted to form a rich database of 160 features.
• Score: 5.080045077714947
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Building height is an important indicator for scientific research and practical application. However, building height products with a high spatial resolution (10m) are still very scarce. To meet the needs of high-resolution building height estimation models, this study established a set of spatial-spectral-temporal feature databases, combining SAR data provided by Sentinel-1, optical data provided by Sentinel-2, and shape data provided by building footprints. The statistical indicators on the time scale are extracted to form a rich database of 160 features. This study combined with permutation feature importance, Shapley Additive Explanations, and Random Forest variable importance, and the final stable features are obtained through an expert scoring system. This study took 12 large, medium, and small cities in the United States as the training data. It used moving windows to aggregate the pixels to solve the impact of SAR image displacement and building shadows. This study built a building height model based on a random forest model and compared three model ensemble methods of bagging, boosting, and stacking. To evaluate the accuracy of the prediction results, this study collected Lidar data in the test area, and the evaluation results showed that its R-Square reached 0.78, which can prove that the building height can be obtained effectively. The fast production of high-resolution building height data can support large-scale scientific research and application in many fields.

Related papers

• Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research [90.91438597133211]
  We introduce WarpSci, a framework designed to overcome crucial system bottlenecks in the application of reinforcement learning. We eliminate the need for data transfer between the CPU and GPU, enabling the concurrent execution of thousands of simulations.
  arXiv  Detail & Related papers  (2024-08-01T21:38:09Z)
• 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)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation [0.0]
  This paper attempts a multitask-learning method of building extraction and height estimation using both optical and radar satellite imagery. Contrary to the initial goal of multitask learning, this paper reports the individual implementation of the building extraction and height estimation under constraints.
  arXiv  Detail & Related papers  (2023-08-05T22:16:19Z)
• 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)
• A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series [0.0]
  In this study, we propose a supervised Multimodal Building Height Network (MBHR-Net) for estimating building heights at 10m spatial resolution using Sentinel-1 (S1) and Sentinel-2 (S2) time series. Our MBHR-Net aims to extract meaningful features from the S1 and S2 images to learn complex-temporal relationships between image patterns and building heights. The model is trained and tested in 10 cities in the Netherlands Root Mean Squared Error (RMSE), Intersection over Union (IoU), and R-squared (R2) score metrics are used to evaluate the performance of the model.
  arXiv  Detail & Related papers  (2023-07-03T22:16:17Z)
• 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)
• 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)
• Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data [7.400745342582259]
  We propose using a generalized sigmoid function to model the distance similarity in both high- and low-dimensional spaces. Using both simulated and real-world data sets, we show that our proposed method can generate visualization results comparable to those of uniform manifold approximation and projection.
  arXiv  Detail & Related papers  (2020-07-17T01:36:45Z)

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.