# Applying Knowledge Transfer for Water Body Segmentation in Peru

arXiv: https://arxiv.org/abs/1912.00957 · HTML: https://mlsanity.com/paper/1912.00957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

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

- [Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture](https://mlsanity.com/paper/2502.05476/) (2025)

## Abstract (arXiv)

In this work, we present the application of convolutional neural networks for segmenting water bodies in satellite images. We first use a variant of the U-Net model to segment rivers and lakes from very high-resolution images from Peru. To circumvent the issue of scarce labelled data, we investigate the applicability of a knowledge transfer-based model that learns the mapping from high-resolution labelled images and combines it with the very high-resolution mapping so that better segmentation can be achieved. We train this model in a single process, end-to-end. Our preliminary results show that adding the information from the available high-resolution images does not help out-of-the-box, and in fact worsen results. This leads us to infer that the high-resolution data could be from a different distribution, and its addition leads to increased variance in our results.