# Improving Image Classification with Location Context

arXiv: https://arxiv.org/abs/1505.03873 · HTML: https://mlsanity.com/paper/1505.03873/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2015.

## Built on by (most cited first, 30 of 33)

- [GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization](https://mlsanity.com/paper/2309.16020/) (2023)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations](https://mlsanity.com/paper/2305.01118/) (2023)
- [Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks](https://mlsanity.com/paper/2310.06743/) (2023)
- [Spatial Implicit Neural Representations for Global-Scale Species Mapping](https://mlsanity.com/paper/2306.02564/) (2023)
- [A Review of Location Encoding for GeoAI: Methods and Applications](https://mlsanity.com/paper/2111.04006/) (2021)
- [Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions](https://mlsanity.com/paper/2306.17624/) (2023)
- [TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning](https://mlsanity.com/paper/2406.15658/) (2024)
- [Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring](https://mlsanity.com/paper/2108.06435/) (2021)
- [Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information](https://mlsanity.com/paper/2203.03253/) (2022)
- [Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications](https://mlsanity.com/paper/2408.14806/) (2024)
- [GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates](https://mlsanity.com/paper/2304.07750/) (2023)
- [BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping](https://mlsanity.com/paper/2310.19168/) (2023)
- [Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias](https://mlsanity.com/paper/2110.10389/) (2021)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [LocDiff: Identifying Locations on Earth by Diffusing in the Hilbert Space](https://mlsanity.com/paper/2503.18142/) (2025)
- [Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery](https://mlsanity.com/paper/2507.13385/) (2025)
- [Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data](https://mlsanity.com/paper/2102.00848/) (2021)
- [Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs](https://mlsanity.com/paper/2106.03774/) (2021)
- [Generating Binary Species Range Maps](https://mlsanity.com/paper/2408.15956/) (2024)
- [Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction](https://mlsanity.com/paper/2410.19256/) (2024)
- [Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions](https://mlsanity.com/paper/2201.10489/) (2022)
- [SLED: Scalable Location Encoding via Distillation](https://mlsanity.com/paper/2608.06612/) (2026)
- [Learning Pixel-Adaptive Weights for Portrait Photo Retouching](https://mlsanity.com/paper/2112.03536/) (2021)
- [Latent Domain Modeling Improves Robustness to Geographic Shifts](https://mlsanity.com/paper/2503.02036/) (2025)
- [From images in the wild to video-informed image classification](https://mlsanity.com/paper/2109.12040/) (2021)
- [CNN-based Classification Framework for Lung Tissues with Auxiliary Information](https://mlsanity.com/paper/2206.06701/) (2022)
- [Leveraging Habitat Information for Fine-grained Bird Identification](https://mlsanity.com/paper/2312.14999/) (2023)
- [Model Hijacking Attack in Federated Learning](https://mlsanity.com/paper/2408.02131/) (2024)
- [SegDesicNet: Lightweight Semantic Segmentation in Remote Sensing with Geo-Coordinate Embeddings for Domain Adaptation](https://mlsanity.com/paper/2503.08290/) (2025)

## Abstract (arXiv)

With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them. In addition to research that tries to predict GPS coordinates from visual features, this also opens up the door to problems that are conditioned on the availability of GPS coordinates. In this work, we tackle the problem of performing image classification with location context, in which we are given the GPS coordinates for images in both the train and test phases. We explore different ways of encoding and extracting features from the GPS coordinates, and show how to naturally incorporate these features into a Convolutional Neural Network (CNN), the current state-of-the-art for most image classification and recognition problems. We also show how it is possible to simultaneously learn the optimal pooling radii for a subset of our features within the CNN framework. To evaluate our model and to help promote research in this area, we identify a set of location-sensitive concepts and annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has GPS coordinates with these concepts, which we make publicly available. By leveraging location context, we are able to achieve almost a 7% gain in mean average precision.