mlsanityData to 14 Oct 2026

Novel Building Detection and Location Intelligence Collection in Aerial Satellite Imagery

arXiv 2302.03156 ↗First posted 6 Feb 2023cs.CV cs.AI eess.IVcited by 0

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Abstract (arXiv)

Building structures detection and information about these buildings in aerial images is an important solution for city planning and management, land use analysis. It can be the center piece to answer important questions such as planning evacuation routes in case of an earthquake, flood management, etc. These applications rely on being able to accurately retrieve up-to-date information. Being able to accurately detect buildings in a bounding box centered on a specific latitude-longitude value can help greatly. The key challenge is to be able to detect buildings which can be commercial, industrial, hut settlements, or skyscrapers. Once we are able to detect such buildings, our goal will be to cluster and categorize similar types of buildings together.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

8 of 23 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
  4. 2018How Does Batch Normalization Help Optimization?cited by 357
  5. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 261
  6. 2018A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decaycited by 226
  7. 2018Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blockscited by 76
  8. 2017Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resizecited by 37

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