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

TL;DR

This paper introduces a fully automated, monocular vision-based framework for accurate, real-scale island area and coastline measurement that reduces costs and improves efficiency without relying on prior GIS data.

Contribution

It presents a novel, geometrically consistent measurement pipeline that uses only place names or coordinates to obtain high-precision island metrics with minimal manual effort.

Findings

Measurement error is around 10% across tested islands.

The system processes each high-resolution image in approximately 70 ms.

The pipeline is verified on four islands with diverse terrain features.

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