DOI: 10.4231/b129-xd47
title: Aerial Fluvial Image Dataset (AFID) for Semantic Segmentation

description: &lt;p&gt;&lt;span dir="ltr" role="presentation" style="left: 327.642px; top: 381.182px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.07318);"&gt;Autonomous navigation of Unmanned Aerial Vehicle (UAV) along and Autonomous&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 401.599px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.01171);"&gt; Surface Vehicle (ASV) within rivers and creeks has been a popular research area in recent years,&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 882.09px; top: 401.599px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.321px; top: 422.018px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.00488);"&gt;where semantic segmentation neural networks have been implemented to recognize the navigable&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.329px; top: 422.018px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 442.435px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.00766);"&gt;space. Currently, it is still difficult to release the power of deep semantic segmentation learning for&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.615px; top: 442.435px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.363px; top: 462.852px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.03161);"&gt;ASV to make long-range navigation plans, and to swiftly and safely do obstacle avoidance while&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.33px; top: 462.852px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 483.271px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.06441);"&gt;navigating in narrow, rapidly flowing and obstacle intensive creeks/rivers due to lack of aerial&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.334px; top: 483.271px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 503.688px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.04111);"&gt;fluvial scene data for supervised training. To tackle this problem, and to enrich the aerial fluvial&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.334px; top: 503.688px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 524.107px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(0.995836);"&gt;semantic segmentation training data, we collected aerial BEV (birds-eye-view) images, with multiple&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.328px; top: 524.107px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 544.524px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.01881);"&gt;camera perspectives, of fluvial scenes with drone that flew above inland waterways. Images have&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.334px; top: 544.524px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 564.941px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.01929);"&gt;been manually selected and semantically labeled with emphasis on extruded obstacles from river&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.622px; top: 564.941px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 585.36px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(0.989128);"&gt;and riverbank, to form the novel dataset with 8 classes (Water, Boat, Bridge, Sky, Forest vegetation,&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 882.098px; top: 585.36px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 605.777px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(0.995842);"&gt;Dry sediment, Drone itself and in-river Obstacles) and 816 high-resolution (2K and 2.7K) images in&lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 880.332px; top: 605.777px; font-size: 14.1136px; font-family: sans-serif;"&gt; &lt;/span&gt;&lt;span dir="ltr" role="presentation" style="left: 261.914px; top: 626.195px; font-size: 14.1136px; font-family: sans-serif; transform: scaleX(1.01946);"&gt;total.&lt;/span&gt;&lt;/p&gt;
description: 816 2K/2.7K per-pixel annotated images with 8 classes: River, Boat, Bridge, Sky, Forest vegetation, Dry sediment, Drone self and Obstacle in river. Fluvial scenes are from Wabash River and Wildcat Creek in Indiana, USA.