Corpus ID: 236428233

Continental-Scale Building Detection from High Resolution Satellite Imagery

@article{Sirko2021ContinentalScaleBD,
  title={Continental-Scale Building Detection from High Resolution Satellite Imagery},
  author={Wojciech Sirko and S. Kashubin and Marvin Ritter and Abigail Annkah and Yasser Salah Edine Bouchareb and Yann Dauphin and Daniel Keysers and Maxim Neumann and Moustapha Ciss{\'e} and John Quinn},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12283}
}
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes. Such information can be particularly useful in developing regions where alternative data sources may be scarce. In this work, we describe a model training pipeline for detecting buildings across the entire continent of Africa, using 50 cm satellite imagery. Starting with the U-Net model, widely used in satellite image analysis, we study variations in architecture, loss functions… Expand

References

SHOWING 1-10 OF 21 REFERENCES
ImageNet Large Scale Visual Recognition Challenge
TLDR
The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared. Expand
U-Net: Convolutional Networks for Biomedical Image Segmentation
TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Expand
COCO-Stuff: Thing and Stuff Classes in Context
TLDR
An efficient stuff annotation protocol based on superpixels is introduced, which leverages the original thing annotations, and the speed versus quality trade-off of the protocol is quantified and the relation between annotation time and boundary complexity is explored. Expand
Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
TLDR
The Naive-Student model, trained with such simple yet effective iterative semi-supervised learning, attains state-of-the-art results at all three Cityscapes benchmarks, reaching the performance of 67.8% PQ, 42.6% AP, and 85.2% mIOU on the test set. Expand
Mask R-CNN
TLDR
This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. Expand
Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images
TLDR
This work introduces the use of the high-resolution network (HRNet) to produce high- resolution features without the decoding stage and enhances the low-to-high features extracted from different branches separately to strengthen the embedding of scale-related contextual information. Expand
Scaled-YOLOv4: Scaling Cross Stage Partial Network
We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy.Expand
Self-Training With Noisy Student Improves ImageNet Classification
We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. OnExpand
Hybrid Task Cascade for Instance Segmentation
TLDR
This work proposes a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Expand
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
  • Ilke Demir, K. Koperski, +6 authors R. Raskar
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2018
TLDR
The DeepGlobe 2018 Satellite Image Understanding Challenge is presented, which includes three public competitions for segmentation, detection, and classification tasks on satellite images, and characteristics of each dataset are analyzed, and evaluation criteria for each task are defined. Expand
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