---
title: Satellites Turn “Concrete”: Tracking Cement with Satellite Data and Neural Networks
url: https://www.ml-quant.com/papers/ssrn/4712741/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4712741
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4712741
featured: 2024-02-07
citations: 5
topic: ML & AI Methods
---


# Satellites Turn “Concrete”: Tracking Cement with Satellite Data and Neural Networks

The study shows that using daily satellite images and machine learning to track economic activity is more effective than traditional models, particularly in the cement and construction industries.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4712741
- Identifier: SSRN 4712741
- Released: 2024-02-01
- First featured: Quant Letter No. 36 (2024-02-07): https://www.ml-quant.com/issues/2024-02-07/
- Citations (Semantic Scholar): 5
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Cement Tracking with Satellites and Neural Networks](https://www.ml-quant.com/papers/repec/bfr-banfra-917/): A new three-step machine learning method for predicting world trade has been proposed, which outperforms traditional linear, non-linear techniques and other benchmark models.
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Graph Mamba: Towards Learning on Graphs with State Space Models](https://www.ml-quant.com/papers/arxiv/2402.08678/): Graph Mamba Networks, a new type of Graph Neural Networks, have been introduced, which achieve excellent performance in various benchmark datasets despite lower computational cost.
- [Edge Directionality Improves Learning on Heterophilic Graphs](https://www.ml-quant.com/papers/arxiv/2305.10498/): The study presents Directed Graph Neural Network (Dir-GNN), a new deep learning framework for directed graphs that surpasses traditional models in heterophilic benchmarks.
- [Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models](https://www.ml-quant.com/papers/arxiv/2310.13828/): Poisoning Attacks on Text-to-Image Models: The article introduces Nightshade, an attack on text-to-image models that can disrupt their functionality, potentially serving as a defense against web scrapers.
- [Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks](https://www.ml-quant.com/papers/arxiv/2310.02244/): Deep Residual Network Feature Learning: The research explores depthwise parametrizations in deep residual networks, pinpointing Depth-$\mu$P as the best parametrization for maximizing feature learning and diversity, but notes its limitations in deeper networks.