# What Goes Where: Predicting Object Distributions from Above

arXiv: https://arxiv.org/abs/1808.00995 · HTML: https://mlsanity.com/paper/1808.00995/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images](https://mlsanity.com/paper/2307.15904/) (2023)
- [BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping](https://mlsanity.com/paper/2310.19168/) (2023)
- [GEOBIND: Binding Text, Image, and Audio through Satellite Images](https://mlsanity.com/paper/2404.11720/) (2024)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)

## Abstract (arXiv)

In this work, we propose a cross-view learning approach, in which images captured from a ground-level view are used as weakly supervised annotations for interpreting overhead imagery. The outcome is a convolutional neural network for overhead imagery that is capable of predicting the type and count of objects that are likely to be seen from a ground-level perspective. We demonstrate our approach on a large dataset of geotagged ground-level and overhead imagery and find that our network captures semantically meaningful features, despite being trained without manual annotations.