Do Deep Learning Models Generalize to Overhead Imagery from Novel Geographic Domains? the xGD Benchmark Problem

Bohao Huang, Kyle Bradbury, Leslie M. Collins, Jordan M. Malof

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, Convolutional Neural Networks (CNNs) have demonstrated impressive performance on several visual recognition benchmark datasets utilizing overhead imagery. However, most of these analyses performed on benchmark datasets involve testing pre-trained CNNs on imagery that was collected over roughly the same locations as the training imagery. In this work we propose a benchmark problem - termed cross-geographical domain (xGD) adaptation - designed to evaluate the performance of CNNs in which they are tested on imagery collected over previously unseen geo-locations - a more challenging and practical scenario that we term cross-domain testing. We focus this work on building segmentation due to the availability of appropriate datasets. The results indicate that CNNs generalize poorly to data processed from geographic locations that were not present in training. Surprisingly, we found that larger models (pre-trained on ImageNet) generalize as well as small models in cross-domain testing, and sometimes better. This work provides the first comprehensive results for cross-domain recognition, raising awareness of this important problem. We hope that xGD can serve as a benchmark for future work; xGD uses publicly-available data, and we release our design details with this publication.

Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1476-1479
Number of pages4
ISBN (Electronic)9781728163741
DOIs
StatePublished - Sep 26 2020
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: Sep 26 2020Oct 2 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa
Period09/26/2010/2/20

Keywords

  • building segmentation
  • domain adaptation
  • overhead imagery
  • segmentation

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