MESSI: A Multi-Elevation Semantic Segmentation Image Dataset of an Urban Environment

TL;DR

The paper introduces MESSI, a diverse multi-elevation drone image dataset for semantic segmentation in urban environments, enabling improved training and evaluation of neural networks for aerial urban scene understanding.

Contribution

It provides a novel, publicly available multi-elevation drone image dataset with detailed annotations for semantic segmentation and related tasks.

Findings

Dataset contains 2525 images from various altitudes and regions.

Semantic segmentation performed using multiple neural network models.

Statistics and evaluation benchmarks are provided for future research.

Abstract

This paper presents a Multi-Elevation Semantic Segmentation Image (MESSI) dataset comprising 2525 images taken by a drone flying over dense urban environments. MESSI is unique in two main features. First, it contains images from various altitudes, allowing us to investigate the effect of depth on semantic segmentation. Second, it includes images taken from several different urban regions (at different altitudes). This is important since the variety covers the visual richness captured by a drone's 3D flight, performing horizontal and vertical maneuvers. MESSI contains images annotated with location, orientation, and the camera's intrinsic parameters and can be used to train a deep neural network for semantic segmentation or other applications of interest (e.g., localization, navigation, and tracking). This paper describes the dataset and provides annotation details. It also explains how semantic segmentation was performed using several neural network models and shows several relevant statistics. MESSI will be published in the public domain to serve as an evaluation benchmark for semantic segmentation using images captured by a drone or similar vehicle flying over a dense urban environment.