# From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps

arXiv: https://arxiv.org/abs/2607.24532 · HTML: https://mlsanity.com/paper/2607.24532/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Foundation Models for Generalist Geospatial Artificial Intelligence](https://mlsanity.com/paper/2310.18660/) (2023)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data](https://mlsanity.com/paper/2507.22291/) (2025)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar](https://mlsanity.com/paper/2304.07213/) (2023)
- [TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis](https://mlsanity.com/paper/2506.20380/) (2025)
- [Major TOM: Expandable Datasets for Earth Observation](https://mlsanity.com/paper/2402.12095/) (2024)
- [Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations](https://mlsanity.com/paper/1805.12219/) (2018)
- [OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation](https://mlsanity.com/paper/2511.13655/) (2025)
- [Estimating Canopy Height at Scale](https://mlsanity.com/paper/2406.01076/) (2024)
- [Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation](https://mlsanity.com/paper/2501.19328/) (2025)
- [Mapping Global Floods with 10 Years of Satellite Radar Data](https://mlsanity.com/paper/2411.01411/) (2024)
- [Facilitating Advanced Sentinel-2 Analysis Through a Simplified Computation of Nadir BRDF Adjusted Reflectance](https://mlsanity.com/paper/2404.15812/) (2024)
- [TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery](https://mlsanity.com/paper/2511.12104/) (2025)
- [ECHOSAT: Estimating Canopy Height Over Space And Time](https://mlsanity.com/paper/2602.21421/) (2026)

## Built on by (most cited first, 1 of 1)

- [Earth Embeddings](https://mlsanity.com/paper/2608.03410/) (2026)

## Abstract (arXiv)

Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (ML) and large computing infrastructure. Although the barrier to generating such maps has dropped substantially, established best practices have yet to emerge, and design decisions made early in the pipeline can quietly propagate errors into the final product. Producing a technically sound and scientifically credible product remains challenging. Choices made at every stage are tightly coupled: preprocessing decisions shape the training signal, dataset design governs what the model can learn and how reliably its performance can be assessed, and global-scale inference introduces engineering challenges in compute and data access at scale, as well as artifact mitigation. Furthermore, uncertainty quantification and independent map validation each require dedicated methodological attention that is often underestimated. This paper presents a concise, end-to-end account of the recommended practices spanning the pipeline from satellite data to an operational map product. We organize the discussion around six interconnected themes: the EO data infrastructure landscape, data selection and preprocessing, ML dataset construction and model training, uncertainty quantification, map production and distribution, and validation. This paper is a condensed version of a longer guide that provides greater depth across all stages, accessible online at ghjuliasialelli.github.io/ML-EO-Maps/.