Automatic Rooftop Detection Using a Two-Stage Classification

Joshi, Bikash; Baluyan, Hayk; Al-Hinai, Amer; Woon, Wei Lee

doi:10.1109/uksim.2014.89

Cited by 7 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The five features are areas, mean intensity value, standard deviation, minor-major ratio, and roundness. The roundness is defined in Equation (1) below [12].…”

Section: Reference Data Preparationmentioning

confidence: 99%

See 1 more Smart Citation

Building Rooftop Extraction Using Machine Learning Algorithms for Solar Photovoltaic Potential Estimation

2023

View full text Add to dashboard Cite

Green cities worldwide are converting to renewable clean energy from natural sources such as sunlight and wind due to the lack of traditional resources and the significant increase in environmental pollution. This paper presents an approach of two stages for photovoltaic (PV) potential estimation of solar panels mounted on buildings’ rooftops. The first stage is rooftop detection from satellite images using a series of image pre-processing algorithms, followed by applying machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The second stage is the solar PV potential estimation using the PVWatts calculator, PVGIS, and ArcGIS. Satellite images for the B6 division of Madinaty City in Egypt were evaluated in this paper. The precision, recall, and F1-score of rooftop detection were 91.2%, 98.6%, and 94.7% from SVM, while those from NB were 86.6%, 98.3%, and 92.2%, respectively. About 290 rooftops were extracted, with a total area of 150,698 m2 and a relative root mean square error of 10.6%. The usable area of rooftops was utilized to estimate the annual PV potential of 21.1, 24.9, and 22.9 GWh/year from the PVWatts calculator, PVGIS, and ArcGIS, respectively. According to the estimated PV potential, replacing traditional energy sources reduced the amount of CO2 by an annual average value of 62%.

show abstract

“…The five features are areas, mean intensity value, standard deviation, minor-major ratio, and roundness. The roundness is defined in Equation (1) below [12].…”

Section: Reference Data Preparationmentioning

confidence: 99%

“…The average F1-score increased to 83.8% after using the histogram method. Joshi et al [12] applied an artificial neural network (ANN) algorithm to classify the segments generated into 'rooftop' and 'non-rooftop' of the same study areas used in [11]. The average F1-score was 86.5%.…”

Section: Introductionmentioning

confidence: 99%

Building Rooftop Extraction Using Machine Learning Algorithms for Solar Photovoltaic Potential Estimation

2023

View full text Add to dashboard Cite

show abstract

“…Applying Deep Learning to Remote Sensed Imagery Multiple projects have leveraged satellite imagery to answer various questions on land use, road quality, object detection, consumption expenditure: by linking sparse ground truth with abundant imagery, researchers can extrapolate trends in existing data to areas where labeled data do not exist [35], [10], [19]. Alternatively, some works have proposed neural network architectures that sidestep training data constraints and the relative lack of labeled ground-truth in remote areas [24] [30].…”

Section: Related Workmentioning

confidence: 99%

Learning to segment from misaligned and partial labels

Fobi¹,

Conlon²,

Taneja³

et al. 2020

Preprint

View full text Add to dashboard Cite

To extract information at scale, researchers increasingly apply semantic segmentation techniques to remotely-sensed imagery. While fully-supervised learning enables accurate pixel-wise segmentation, compiling the exhaustive datasets required is often prohibitively expensive. As a result, many non-urban settings lack the ground-truth needed for accurate segmentation. Existing open source infrastructure data for these regions can be inexact and non-exhaustive. Open source infrastructure annotations like OpenStreetMaps are representative of this issue: while OpenStreetMaps labels provide global insights to road and building footprints, noisy and partial annotations limit the performance of segmentation algorithms that learn from them.In this paper, we present a novel and generalizable two-stage framework that enables improved pixel-wise image segmentation given misaligned and missing annotations. First, we introduce the Alignment Correction Network to rectify incorrectly registered open source labels. Next, we demonstrate a segmentation model -the Pointer Segmentation Network -that uses corrected labels to predict infrastructure footprints despite missing annotations. We test sequential performance on the Aerial Imagery for Roof Segmentation dataset, achieving a mean intersection-over-union score of 0.79; more importantly, model performance remains stable as we decrease the fraction of annotations present. We demonstrate the transferability of our method to lower quality data sources, by applying the Alignment Correction Network to OpenStreetMaps labels to correct building footprints; we also demonstrate the accuracy of the Pointer Segmentation Network in predicting cropland boundaries in California from medium resolution data. Overall, our methodology is robust for multiple applications with varied amounts of training data present, thus offering a method to extract reliable information from noisy, partial data.

show abstract

“…Early efforts in automatic rooftop segmentation relied on techniques to generate candidate rooftops and subsequent evaluation to accept or reject candidate rooftops. Edge detection, corner detection, and segmentation into homogeneous regions via k-means clustering or support Vector Machines (SVM) have been used to identify candidate rooftops [8], [9]. Discriminative features used to evaluate candidate rooftops include building shadows, geometry and spectral characteristics [10], [11].…”

Section: Related Workmentioning

confidence: 99%

Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

Collier

Duffy

Ganguly

et al. 2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

View full text Add to dashboard Cite

Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2dataset. Progressive GAN training achieved a test accuracy of 93% compared to 89% for traditional GAN training.

show abstract

Automatic Rooftop Detection Using a Two-Stage Classification

Cited by 7 publications

References 12 publications

Building Rooftop Extraction Using Machine Learning Algorithms for Solar Photovoltaic Potential Estimation

Building Rooftop Extraction Using Machine Learning Algorithms for Solar Photovoltaic Potential Estimation

Learning to segment from misaligned and partial labels

Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

Contact Info

Product

Resources

About