• Corpus ID: 239885980

Cross-Region Building Counting in Satellite Imagery using Counting Consistency

  title={Cross-Region Building Counting in Satellite Imagery using Counting Consistency},
  author={Muaaz Zakria and Hamza Rawal and Waqas Sultani and Mohsen Ali},
Estimating the number of buildings in any geographical region is a vital component of urban analysis, disaster management, and public policy decision. Deep learning methods for building localization and counting in satellite imagery, can serve as a viable and cheap alternative. However, these algorithms suffer performance degradation when applied to the regions on which they have not been trained. Current large datasets mostly cover the developed regions and collecting such datasets for every… 


Abstract. Estimating the spatio-temporal profile of a building’s construction using high-resolution satellite images is a critical problem since it can be utilized for a variety of data-driven urban

Centerpoints Are All You Need in Overhead Imagery

Novel single- and two-stage network architectures that use centerpoints for labeling achieve nearly equivalent performance to approaches using more detailed labeling on three overhead object detection datasets.

Counting Everything in Remote Sensing - the Need for Benchmarks

Object counting and object density estimation is a basic technology serving many applications. For that reason, a multitude of benchmarks are existing in the computer vision community allowing to



Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery

  • J. IqbalMohsen Ali
  • Computer Science, Environmental Science
    ISPRS Journal of Photogrammetry and Remote Sensing
  • 2020

Destruction from sky: Weakly supervised approach for destruction detection in satellite imagery

Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

An algorithm is designed that reduces the domain shift impact using Generative Adversarial Networks (GANs) and improves the average segmentation accuracy of the inverted classes due to sensor variation, which is being of great potential in surveillance and scene understanding of urban areas.

SpaceNet: A Remote Sensing Dataset and Challenge Series

It is proposed that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques.

Towards Perspective-Free Object Counting with Deep Learning

A novel convolutional neural network solution, named Counting CNN (CCNN), formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps, able to estimate object densities in different very crowded scenarios.

ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

A novel semantic segmentation framework that can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to the distribution of the test images.

Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

Deep learning models are shown to be able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution imagery with errors comparable to that of existing ground data.

GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

With the proposed simple MCNN model, the method outperforms all existing methods and experiments show that the model, once trained on one dataset, can be readily transferred to a new dataset.

A MultiKernel Domain Adaptation Method for Unsupervised Transfer Learning on Cross-Source and Cross-Region Remote Sensing Data Classification

  • Wei LiuR. Qin
  • Computer Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2020
This article proposes a novel DA method for unsupervised TL, namely, multikernel jointly domain matching (MKJDM), which by definition considers multiple kernels as opposed to the currently popular single-kernel methods for measuring the distances between distributions.