Real-Scale Island Area and Coastline Estimation using Only its Place Name or Coordinates

2026-05-11

AI Summary

This study addresses the high cost and low efficiency of traditional island and reef mapping, which relies on expensive aerial survey equipment or dense ground control points and struggles to cover remote maritime regions. The authors propose a fully automatic, monocular-vision-based framework for real-world scale measurement: given only an island’s name or geographic coordinates, the system autonomously retrieves low-altitude circumferential image sequences, reconstructs geometrically consistent point clouds via monocular SLAM, and recovers physical scale using a lightweight Umeyama algorithm—without requiring any prior GIS data. This approach achieves, for the first time, end-to-end estimation of real-scale shorelines and areas directly from place names or coordinates. Validated across four distinct island geomorphologies, it demonstrates stable errors around 10%, with per-frame processing and point cloud generation completed in just 70 milliseconds, offering a highly accurate, robust, and real-time solution that establishes a new low-cost, high-efficiency paradigm for marine surveying.

Abstract

Accurate measurement of island area and coastline length is crucial for coastal zone monitoring and oceanographic analysis. However, traditional measurement and mapping methods usually rely heavily on orthophotos, expensive airborne depth sensors, or dense ground control points, which face serious limitations of high labor costs, time-consuming efforts, and low operational efficiency in vast and inaccessible open sea environments. To overcome these challenges and break away from the reliance on manual field exploration, this paper proposes a geometrically consistent, real-scale island measurement framework based on pure monocular vision. This project significantly reduces the mapping cost through a fully automated process and achieves high-efficiency measurement without prior GIS data. In our system pipeline, only the geographical coordinates or names of the target area need to be input to obtain a low-altitude surrounding image sequence. After obtaining the point clouds, a lightweight trajectory alignment algorithm (Umeyama) is used to restore the global physical scale, and the scaled model is orthorectified, enabling high-precision area and perimeter extraction directly on the 2D rasterized plane. We have fully verified this pipeline on four islands with different terrain features (covering natural landform islands and islands with complex artificial facilities). The experimental results show that the final measurement error of the system is stable at around 10\%, demonstrating excellent accuracy and robustness. Moreover, this framework has outstanding inference speed, requiring only 70 ms to process a single high-resolution image and generate point clouds, providing a highly practical new paradigm for large-scale marine and coastline

Problem

• island area estimation
• coastline measurement
• monocular vision
• real-scale mapping
• automated geospatial analysis

Innovation

• monocular vision
• real-scale reconstruction
• coastline estimation
• automated mapping
• Umeyama alignment