# Learning a Dynamic Map of Visual Appearance

arXiv: https://arxiv.org/abs/2012.14885 · HTML: https://mlsanity.com/paper/2012.14885/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 18 of 18)

- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning](https://mlsanity.com/paper/2307.06166/) (2023)
- [Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images](https://mlsanity.com/paper/2307.15904/) (2023)
- [Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information](https://mlsanity.com/paper/2203.03253/) (2022)
- [Revisiting Near/Remote Sensing with Geospatial Attention](https://mlsanity.com/paper/2204.01807/) (2022)
- [BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping](https://mlsanity.com/paper/2310.19168/) (2023)
- [GT-Loc: Unifying When and Where in Images Through a Joint Embedding Space](https://mlsanity.com/paper/2507.10473/) (2025)
- [Content-Aware Detection of Temporal Metadata Manipulation](https://mlsanity.com/paper/2103.04736/) (2021)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [Using Social Media Images for Building Function Classification](https://mlsanity.com/paper/2202.07315/) (2022)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review](https://mlsanity.com/paper/2402.15448/) (2024)
- [CVD-SfM: A Cross-View Deep Front-end Structure-from-Motion System for Sparse Localization in Multi-Altitude Scenes](https://mlsanity.com/paper/2508.01936/) (2025)
- [TIGeR: A Unified Framework for Time, Images and Geo-location Retrieval](https://mlsanity.com/paper/2603.24749/) (2026)
- [What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images](https://mlsanity.com/paper/2503.17899/) (2025)
- [Probabilistic Image-Driven Traffic Modeling via Remote Sensing](https://mlsanity.com/paper/2403.05521/) (2024)
- [SegDesicNet: Lightweight Semantic Segmentation in Remote Sensing with Geo-Coordinate Embeddings for Domain Adaptation](https://mlsanity.com/paper/2503.08290/) (2025)
- [GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction](https://mlsanity.com/paper/2601.13207/) (2026)

## Abstract (arXiv)

The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained understanding of the expected appearance at any geographic location and time. Our approach integrates dense overhead imagery with location and time metadata into a general framework capable of mapping a wide variety of visual attributes. A key feature of our approach is that it requires no manual data annotation. We demonstrate how this approach can support various applications, including image-driven mapping, image geolocalization, and metadata verification.