# CrossVIT-augmented Geospatial-Intelligence Visualization System for Tracking Economic Development Dynamics

arXiv: https://arxiv.org/abs/2412.10474 · HTML: https://mlsanity.com/paper/2412.10474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text](https://mlsanity.com/paper/2104.11178/) (2021)
- [A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery](https://mlsanity.com/paper/2010.08168/) (2020)
- [Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale](https://mlsanity.com/paper/1704.02965/) (2017)
- [Manipulation Detection in Satellite Images Using Vision Transformer](https://mlsanity.com/paper/2105.06373/) (2021)
- [Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access](https://mlsanity.com/paper/2109.02890/) (2021)
- [Learning Economic Indicators by Aggregating Multi-Level Geospatial Information](https://mlsanity.com/paper/2205.01472/) (2022)
- [Predicting Levels of Household Electricity Consumption in Low-Access Settings](https://mlsanity.com/paper/2112.08497/) (2021)
- [Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain](https://mlsanity.com/paper/2203.01068/) (2022)

## Abstract (arXiv)

Timely and accurate economic data is crucial for effective policymaking. Current challenges in data timeliness and spatial resolution can be addressed with advancements in multimodal sensing and distributed computing. We introduce Senseconomic, a scalable system for tracking economic dynamics via multimodal imagery and deep learning. Built on the Transformer framework, it integrates remote sensing and street view images using cross-attention, with nighttime light data as weak supervision. The system achieved an R-squared value of 0.8363 in county-level economic predictions and halved processing time to 23 minutes using distributed computing. Its user-friendly design includes a Vue3-based front end with Baidu maps for visualization and a Python-based back end automating tasks like image downloads and preprocessing. Senseconomic empowers policymakers and researchers with efficient tools for resource allocation and economic planning.