Spatially Adaptive Convolutional Networks with Coordinate-Conditioned Layers
Pages 549 - 552
Abstract
In this study, we present a convolutional neural network (CNN) architecture, GeoConv, designed to improve the accuracy and adaptability of deep learning models using satellite imagery. Traditional CNNs, such as ResNet18, employ fixed-weight convolutional layers - i.e., layers that leverage the same set of weights for each input observation. However, these models can struggle to capture context-specific features inherent in satellite images, which may vary significantly across different geographic regions. To address this challenge, the GeoConv model utilizes dynamic weights that adapt based on the input image coordinates, allowing the model to tailor its feature extraction process to the unique characteristics of different geographic regions. Through experiments, we illustrate the utility of this approach in a case study which leverages satellite imagery to estimate household wealth across 11 countries, with GeoConv explaining an additional 10.12% of the variance in the data compared to a ResNet18 model. These results underscore the importance of incorporating spatially adaptive mechanisms in handling the variability present in satellite imagery. Code is available at: https://github.com/heatherbaier/geoconv
References
[1]
2023. https://www.usaid.gov/global-health/demographic-and-health-surveys-program
[2]
2023. PlanetScope Satellite Imagery. https://www.planet.com/. Accessed: 2023-09-30.
[3]
Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David Lobell, and Stefano Ermon. 2021. Efficient poverty mapping from high resolution remote sensing images. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 12--20.
[4]
Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, and Tom Swartz. 2017. Poverty mapping using convolutional neural networks trained on high and medium resolution satellite images, with an application in Mexico. arXiv preprint arXiv:1711.06323 (2017).
[5]
Carmen Delgado-Viñas and María-Luisa Gómez-Moreno. 2022. The Interaction between Urban and Rural Areas: An Updated Paradigmatic, Methodological and Bibliographic Review. Land 11, 8 (2022), 1298. https://doi.org/10.3390/land11081298
[6]
Jayant Gupta, Carl Molnar, Yiqun Xie, Joe Knight, and Shashi Shekhar. 2021. Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach. ACM Trans. Intell. Syst. Technol. 12, 6, Article 76 (nov 2021), 21 pages. https://doi.org/10.1145/3466688
[7]
David Ha, Andrew Dai, and Quoc V Le. 2016. Hypernetworks. arXiv preprint arXiv:1609.09106 (2016).
[8]
Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, and Stefano Ermon. 2016. Combining satellite imagery and machine learning to predict poverty. Science 353, 6301 (2016), 790--794. https://doi.org/10.1126/science.aaf7894 arXiv:https://www.science.org/doi/pdf/10.1126/science.aaf7894
[9]
Zhe Jiang, Arpan Man Sainju, Yan Li, Shashi Shekhar, and Joseph Knight. 2019. Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity. ACM Trans. Intell. Syst. Technol. 10, 4, Article 43 (aug 2019), 25 pages. https://doi.org/10.1145/3337798
[10]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.
[11]
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. 1989. Backpropagation applied to handwritten zip code recognition. Neural computation 1, 4 (1989), 541--551.
[12]
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278--2324.
[13]
Zheyi Pan, Yuxuan Liang, Junbo Zhang, Xiuwen Yi, Yong Yu, and Yu Zheng. 2018. Hyperst-net: Hypernetworks for spatio-temporal forecasting. arXiv preprint arXiv:1809.10889 (2018).
[14]
Anthony Perez, Swetava Ganguli, Stefano Ermon, George Azzari, Marshall Burke, and David Lobell. 2019. Semi-supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks (gans) for predicting poverty. arXiv preprint arXiv:1902.11110 (2019).
[15]
D Runfola, A Stefanidis, and H Baier. 2022. Using satellite data and deep learning to estimate educational outcomes in data-sparse environments. Remote Sensing Letters 13, 1 (2022), 87--97.
[16]
Dan Runfola, Anthony Stefanidis, Zhonghui Lv, Joseph O'Brien, and Heather Baier. 2024. A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance. International Journal of Geographical Information Science (2024), 1--25.
[17]
Yiqun Xie, Erhu He, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh, and Praveen Ravirathinam. 2021. A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity. In 2021 IEEE International Conference on Data Mining (ICDM). 767--776. https://doi.org/10.1109/ICDM51629.2021.00088
[18]
Zhitong Xiong, Yi Wang, Fahong Zhang, Adam J. Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, and Xiao Xiang Zhu. 2024. Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities. http://arxiv.org/abs/2403.15356 arXiv:2403.15356 [cs].
[19]
Xuan Zhou and Xuefeng Wei. 2023. Feature Aggregation Network for Building Extraction from High-Resolution Remote Sensing Images. arXiv preprint arXiv:2309.06017 (2023).
Index Terms
- Spatially Adaptive Convolutional Networks with Coordinate-Conditioned Layers
Recommendations
Effectiveness of convolutional layers in pre-trained models for classifying common weeds in groundnut and corn crops
Highlights- A new balanced and multi-class dataset for groundnut crops with 15 common weeds has been taken from different locations in Andrapradesh and Tamilnadu states ...
AbstractIn modern agriculture, herbicides are most commonly used to control weeds. A large amount of herbicide usage not only has an adverse effect on the soil but also has a severe environmental impact. A smart herbicide sprayer combines the ...
Graphical abstractDisplay Omitted
Ensemble of fine‐tuned convolutional neural networks for urine sediment microscopic image classification
In this study, an ensemble of fine‐tuned convolutional neural networks (CNNs) is proposed. As CNN training requires large annotated data, which are lacking in the field of urine sediment microscopic image processing, the authors first pre‐trained the CNNs,...
Adaptive Gabor convolutional networks
Highlights- AGCNs use Gabor filters to manipulate convolutional kernels.
- We have proved that AGCNs can learn the invariant information from images.
- The parameters in the Gabor filters can be updated during the model training.
- We ...
Graphical abstractDisplay Omitted
AbstractDespite the great breakthroughs that deep convolutional neural networks (DCNNs) have achieved on image representation learning in recent years, they lack the ability to extract invariant information from images. On the other hand, several ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2024
743 pages
ISBN:9798400711077
DOI:10.1145/3678717
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 November 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
SIGSPATIAL '24
Sponsor:
SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems
October 29 - November 1, 2024
GA, Atlanta, USA
Acceptance Rates
SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 43Total Downloads
- Downloads (Last 12 months)43
- Downloads (Last 6 weeks)43
Reflects downloads up to 21 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in