# Revisiting Near/Remote Sensing with Geospatial Attention

arXiv: https://arxiv.org/abs/2204.01807 · HTML: https://mlsanity.com/paper/2204.01807/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 64 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Wide-Area Image Geolocalization with Aerial Reference Imagery](https://mlsanity.com/paper/1510.03743/) (2015)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [Epipolar Transformers](https://mlsanity.com/paper/2005.04551/) (2020)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Localizing and Orienting Street Views Using Overhead Imagery](https://mlsanity.com/paper/1608.00161/) (2016)
- [Predicting Ground-Level Scene Layout from Aerial Imagery](https://mlsanity.com/paper/1612.02709/) (2016)
- [Learn To Pay Attention](https://mlsanity.com/paper/1804.02391/) (2018)
- [Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery](https://mlsanity.com/paper/2103.01623/) (2021)
- [Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution](https://mlsanity.com/paper/1905.01752/) (2019)
- [SkyScapes -- Fine-Grained Semantic Understanding of Aerial Scenes](https://mlsanity.com/paper/2007.06102/) (2020)
- [Learning a Dynamic Map of Visual Appearance](https://mlsanity.com/paper/2012.14885/) (2020)
- [A Unified Model for Near and Remote Sensing](https://mlsanity.com/paper/1708.03035/) (2017)
- [Understanding and Mapping Natural Beauty](https://mlsanity.com/paper/1612.03142/) (2016)
- [Towards seamless multi-view scene analysis from satellite to street-level](https://mlsanity.com/paper/1705.08101/) (2017)
- [Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images](https://mlsanity.com/paper/1802.02668/) (2018)
- [Dynamic Traffic Modeling From Overhead Imagery](https://mlsanity.com/paper/2012.10530/) (2020)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [Land Use Classification using Convolutional Neural Networks Applied to Ground-Level Images](https://mlsanity.com/paper/1609.06653/) (2016)

## Built on by (most cited first, 12 of 12)

- [Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs](https://mlsanity.com/paper/2303.14672/) (2023)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic Segmentation](https://mlsanity.com/paper/2404.02638/) (2024)
- [Mutation-Based Adversarial Attacks on Neural Text Detectors](https://mlsanity.com/paper/2302.05794/) (2023)
- [Seeing through Satellite Images at Street Views](https://mlsanity.com/paper/2505.17001/) (2025)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Mixed-View Panorama Synthesis using Geospatially Guided Diffusion](https://mlsanity.com/paper/2407.09672/) (2024)
- [Handling Image and Label Resolution Mismatch in Remote Sensing](https://mlsanity.com/paper/2211.15790/) (2022)
- [Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review](https://mlsanity.com/paper/2402.15448/) (2024)
- [Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function](https://mlsanity.com/paper/2307.15230/) (2023)
- [Probabilistic Image-Driven Traffic Modeling via Remote Sensing](https://mlsanity.com/paper/2403.05521/) (2024)
- [Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning](https://mlsanity.com/paper/2408.09460/) (2024)

## Abstract (arXiv)

This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods.