Illi Adil, Bouzaachane Khadija, El Hadaj Salah, El Guarmah El Mahdi
FST, Cadi Ayyad University, L2IS Marrakesh, Morocco.
ENCG, Cadi Ayyad University, L2IS Marrakesh, Morocco.
Data Brief. 2024 Apr 2;54:110379. doi: 10.1016/j.dib.2024.110379. eCollection 2024 Jun.
Detecting emergency aircraft landing sites is crucial for ensuring passenger and crew safety during unexpected forced landings caused by factors like engine malfunctions, adverse weather, or other aviation emergencies. In this article, we present a dataset consisting of Google Maps images with their corresponding masks, specifically crafted with manual annotations of emergency aircraft landing sites, distinguishing between safe areas with suitable conditions for emergency landings and unsafe areas presenting hazardous conditions. Drawing on detailed guidelines from the Federal Aviation Administration, the annotations focus on key features such as slope, surface type, and obstacle presence, with the goal of pinpointing appropriate landing areas. The proposed dataset has 4180 images, with 2090 raw images accompanied by their corresponding annotation instances. This dataset employs a semantic segmentation approach, categorizing the image pixels into two "Safe" and "Unsafe" classes based on authenticated terrain-specific attributes, thereby offering a nuanced understanding of the viability of various landing sites in emergency scenarios.
检测紧急飞机降落场地对于在发动机故障、恶劣天气或其他航空紧急情况等因素导致的意外迫降期间确保乘客和机组人员的安全至关重要。在本文中，我们展示了一个数据集，该数据集由谷歌地图图像及其相应的掩码组成，这些图像经过专门人工标注了紧急飞机降落场地，区分出具有适合紧急降落条件的安全区域和呈现危险状况的不安全区域。根据美国联邦航空管理局的详细指南，标注聚焦于诸如坡度、地面类型和障碍物存在等关键特征，目的是确定合适的降落区域。所提出的数据集有4180张图像，其中2090张原始图像伴有相应的标注实例。该数据集采用语义分割方法，根据经认证的特定地形属性将图像像素分类为“安全”和“不安全”两类，从而在紧急情况下对各种降落场地的可行性提供细致入微的理解。
