A GIS Pipeline to Produce GeoAI Datasets from Drone Overhead Imagery

Abstract

Drone imagery is becoming the main source of overhead information to support decisions in many different fields, especially with deep learning integration. Datasets to train object detection and semantic segmentation models to solve geospatial data analysis are called GeoAI datasets. They are composed of images and corresponding labels represented by full-size masks typically obtained by manual digitizing. GIS software is made of a set of tools that can be used to automate tasks using geo-referenced raster and vector layers. This work describes a workflow using GIS tools to produce GeoAI datasets. In particular, it mentions the steps to obtain ground truth data from OSM and use methods for geometric and spectral augmentation and the data fusion of drone imagery. A method semi-automatically produces masks for point and line objects, calculating an optimum buffer distance. Tessellation into chips, pairing and imbalance checking is performed over the image–mask pairs. Dataset splitting into train–validation–test data is done randomly. All of the code for the different methods are provided in the paper, as well as point and road datasets produced as examples of point and line geometries, and the original drone orthomosaic images produced during the research. Semantic segmentation results performed over the point and line datasets using a classical U-Net show that the semi-automatically produced masks, called primitive masks, obtained a higher mIoU compared to other equal-size masks, and almost the same mIoU metric compared to full-size manual masks.

Species-Level Classification of Peatland Vegetation Using Ultra-High-Resolution UAV Imagery

“…That said, in comparison to traditional algorithms, GeoAI is a novel and developing field [59]. AI methods are often less accessible than traditional techniques, particularly for UAV-based studies (although frameworks are starting to be developed, e.g., [63]). At present, there is a lack of published literature comparing the two approaches for species-level classifications in peatlands, or even similar (e.g., wetland, grassland) ecosystems.…”

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

“…The key applications of GeoAI for flood prediction [69][70][71] (Figure 3) are (i) Flood Hazard Mapping-GeoAI models are employed to predict areas susceptible to flooding and to identify the zones likely to be inundated, providing critical information for preparedness, evacuation, and infrastructure protection; (ii) Flood Extent Forecasting-these models predict the geographic spread and depth of inundation in near-real time. This facilitates targeted emergency response and damage assessment; (iii) Early Warning Systems-the integration of AI with weather forecasting can lead to timely and accurate flood alerts, allowing for pre-emptive measures and saving lives [72]; and (iv) Scenario-Based Modeling-researchers employ GeoAI to simulate potential flooding under different climate change scenarios, informing long-term land-use planning, infrastructure hardening, and protective measures [73].…”

Deep Learning Model Analysis of Drone Images for Unauthorized Occupancy Detection of River Site

“…The GeoAI dataset (Ballesteros, Sanchez-Torres, and Branch-Bedoya, 2022) was used to train object detection and semantic segmentation models for geospatial data analysis. Liao and Juang (2022) proposed a monitoring system that uses drones in real-time to detect waste on beaches and in the ocean.…”