# Clutter Classification Using Deep Learning in Multiple Stages

arXiv: https://arxiv.org/abs/2408.04407 · HTML: https://mlsanity.com/paper/2408.04407/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Machine Learning Approach to RF Transmitter Identification](https://mlsanity.com/paper/1711.01559/) (2017)
- [Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data](https://mlsanity.com/paper/1803.11266/) (2018)

## Abstract (arXiv)

Path loss prediction for wireless communications is highly dependent on the local environment. Propagation models including clutter information have been shown to significantly increase model accuracy. This paper explores the application of deep learning to satellite imagery to identify environmental clutter types automatically. Recognizing these clutter types has numerous uses, but our main application is to use clutter information to enhance propagation prediction models. Knowing the type of obstruction (tree, building, and further classifications) can improve the prediction accuracy of key propagation metrics such as path loss.