Flood and Waterfront Infrastructure Segmentation Dataset (FWISD)

1. Dataset Overview

The Flood and Waterfront Infrastructure Segmentation Dataset (FWISD) is constructed for post-disaster assessment, specifically focusing on the impact of Hurricane Francine (September 2024). This dataset utilizes high-resolution UAV imagery to enable precise semantic segmentation of floodwaters, infrastructure damage, and environmental elements.

• Total Data Size: ~4.36 GB
• Image Resolution: 1024 x 1024 pixels
• Source: NOAA UAV Imagery
• Task: Semantic Segmentation (12 Classes)

2. Data Collection & Context

The data collection centers on Hurricane Francine during the 2024 Atlantic hurricane season. On September 11, 2024, a Category 2 hurricane originating in the Atlantic struck the southern Louisiana coast. The event cut power to over 163,000 residents and triggered widespread flooding. The hurricane's 3-meter storm surge and 304 mm of rainfall severely threatened coastal infrastructure. As Louisiana is a vital trade hub located at the Mississippi River's mouth, a swift and precise assessment of the region is important.

This study utilized UAV imagery released by the U.S. National Oceanic and Atmospheric Administration (NOAA) in the aftermath of the disaster. The image data were collected between September 16 and 17, 2024, covering multiple severely affected areas in southern Louisiana.

3. Class Definitions

To construct a high-quality segmentation dataset, 12 target categories were defined (11 objects + 1 background). They are organized logically from natural environmental elements to man-made infrastructure, movable objects, and disaster-specific elements.

ID	Class Name	Definition
0	Background	Regions that do not belong to any of the 11 defined classes, such as unidentifiable debris or clutter.
1	Natural Water	Pre-existing, permanent water bodies within the scene, such as rivers, lakes, and other natural reservoirs.
2	Tree	Various forms of arbor (trees) and taller shrub vegetation.
3	Road-Passable	Road segments, including highways, streets, and bridges, where the road surface is clearly visible and not submerged by floodwater.
4	Road-Flooded	Road segments that are partially or entirely covered by floodwater.
5	Building-Intact	Buildings retaining their structural integrity or exhibiting only minor damage, with no obvious collapse or significant breaches in major load-bearing elements.
6	Building-Damaged	Buildings exhibiting evident structural failure, characterized by partial or total roof loss, wall collapse, or significant structural deformation.
7	Waterfront Structure-Intact	Facilities (e.g., piers, jetties, docks) that interface with water bodies and remain structurally sound and undamaged.
8	Waterfront Structure-Damaged	Waterfront facilities exhibiting structural failure, such as breakage, collapse, or severe degradation due to flood or water damage.
9	Vehicle-Land	Conveyances situated on terrestrial surfaces, including roads, parking areas, or dry ground.
10	Vehicle-Water	Conveyances located within natural water bodies.
11	Floodwater	Transient accumulation of water over land areas (e.g., roads, vegetated areas, building perimeters) resulting from hurricanes or heavy rainfall.

4. Annotation & Quality Control

We designed a standardized pipeline to ensure pixel-level labeling accuracy using LabelMe software. A multi-round iterative quality control mechanism was implemented:

1. Standardization: Clear textual definitions and typical visual examples were provided to the annotation team.
2. Iterative Review:
   • Round 1: Annotator self-inspection and preliminary correction.
   • Round 2: Manager review focusing on misclassification, omission, and boundary precision. Samples with errors were returned for correction.
   • Round 3: Final inspection to ensure all issues were addressed.
3. Result: This closed-loop process ensures sharp boundaries and accurate class assignments.

5. Directory Structure

The dataset follows a standard semantic segmentation directory structure. Images and Masks are matched by filenames.