Rapid building detection using machine learning

TL;DR

This paper presents a machine learning-based approach for rapid building detection in low-quality RGB geospatial imagery, utilizing innovative candidate search, feature extraction, and a new Permutable Haar Mesh to improve detection accuracy and efficiency.

Contribution

It introduces a novel candidate generation and alignment method, along with the Permutable Haar Mesh, to enhance building detection performance in large-scale geospatial imagery.

Findings

Achieved 80-85% coverage of buildings with candidate stitching techniques.

Boosted classification precision to 80-90% with a linear time candidate alignment.

Demonstrated effective large-scale building detection in low-quality imagery.

Abstract

This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imagery is available. We utilize new candidate search and feature extraction techniques to reduce the problem to a machine learning (ML) classification task. Here we can harness the complex patterns of contrast features contained in training data to establish a model of buildings. We avoid costly sliding windows to generate candidates; instead we innovatively stitch together well known image processing techniques to produce candidates for building detection that cover 80-85% of buildings. Reducing the number of possible candidates is important due to the scale of the problem. Each candidate is subjected to classification which, although linear, costs time and prohibits large scale evaluation. We propose a candidate alignment algorithm to boost classification performance to 80-90% precision with a linear time algorithm and show it has negligible cost. Also, we propose a new concept called a Permutable Haar Mesh (PHM) which we use to form and traverse a search space to recover candidate buildings which were lost in the initial preprocessing phase.