构建了2525张城市多高度无人机图像数据集，用于研究深度对语义分割的影响。
MESSI: A Multi-Elevation Semantic Segmentation Image Dataset of an Urban Environment
- 采集自不同高度和城市的无人机图像，覆盖复杂三维场景
- 包含位置、朝向、相机参数等元数据，支持精准建模
- 适合研究无人机导航、定位与城市环境理解的科研人员
本文提出一个名为MESSI的多高度语义分割图像数据集，包含2525张无人机在密集城市环境中拍摄的图像。该数据集具有两大特点：一是涵盖多种飞行高度，可用于研究深度对语义分割的影响；二是覆盖多个不同城市区域（在不同高度拍摄），充分反映无人机3D飞行时的视觉多样性。所有图像均标注了位置、朝向及相机内参，可用来训练深度神经网络进行语义分割，也可用于定位、导航与跟踪等应用。本文详细描述了数据集构建过程与标注规范，并展示了使用多种神经网络模型进行语义分割的结果及关键统计信息。MESSI将公开发布，作为评估无人机或类似平台在密集城市环境中进行图像语义分割的基准。
原文摘要 · Abstract (English)
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.
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