# TerraTrace: Temporal Signature Land Use Mapping System

arXiv: https://arxiv.org/abs/2502.18704 · HTML: https://mlsanity.com/paper/2502.18704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery](https://mlsanity.com/paper/2311.17179/) (2023)
- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (2023)
- [PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs](https://mlsanity.com/paper/2303.08954/) (2023)
- [Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach](https://mlsanity.com/paper/2401.15875/) (2024)

## Abstract (arXiv)

Understanding land use over time is critical to tracking events related to climate change, like deforestation. However, satellite-based remote sensing tools which are used for monitoring struggle to differentiate vegetation types in farms and orchards from forests. We observe that metrics such as the Normalized Difference Vegetation Index (NDVI), based on plant photosynthesis, have unique temporal signatures that reflect agricultural practices and seasonal cycles. We analyze yearly NDVI changes on 20 farms for 10 unique crops. Initial results show that NDVI curves are coherent with agricultural practices, are unique to each crop, consistent globally, and can differentiate farms from forests. We develop a novel longitudinal NDVI dataset for the state of California from 2020-2023 with 500~m resolution and over 70 million points. We use this to develop the TerraTrace platform, an end-to-end analytic tool that classifies land use using NDVI signatures and allows users to query the system through an LLM chatbot and graphical interface.