# Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation

Source: [Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa77be1e209131cea750f3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:25:31.619Z

Authors: Anas Berka, Mohamed El Hajji, Raphael Canals, Youssef Es-saady, Adel Hafiane

Published: 2025-03-28T23:07:39.000Z

## Abstract

Aerial and satellite imagery are inherently complementary remote sensing sources, offering high-resolution detail alongside expansive spatial coverage. However, the use of these sources for land cover segmentation introduces several challenges, prompting the development of a variety of segmentation methods. Among these approaches, the DeepLabV3+ architecture is considered as a promising approach in the field of single-source image segmentation. However, despite its reliable results for segmentation, there is still a need to increase its robustness and improve its performance. This is particularly crucial for multimodal image segmentation, where the fusion of diverse types of information is essential.   An interesting approach involves enhancing this architectural framework through the integration of novel components and the modification of certain internal processes.   In this paper, we enhance the DeepLabV3+ architecture by introducing a new transposed conventional layers block for upsampling a second entry to fuse it with high level features. This block is designed to amplify and integrate information from satellite images, thereby enriching the segmentation process through fusion with aerial images.   For experiments, we used the LandCover.ai (Land Cover from Aerial Imagery) dataset for aerial images, alongside the corresponding dataset sourced from Sentinel 2 data.   Through the fusion of both sources, the mean Intersection over Union (mIoU) achieved a total mIoU of 84.91% without data augmentation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.22909>)

[PDF](<https://arxiv.org/pdf/2503.22909.pdf>)