DET: Data Enhancement Technique for Aerial Images

Deep learning and computer vision are two thriving research areas within machine learning. In recent years, as the available computing power has grown, it has led to the possibility of combining the approaches, achieving state-of-the-art results. An area of research that has greatly benefited from this development is building detection. Although the algorithms produce satisfactory results, there are still many limitations. One significant problem is the quality and edge sharpness of the segmentation masks, which are not up to the standard required by the mapping industry. The predicted mask boundaries need to be sharper and more precise to have practical use in map production. This thesis introduces a novel Data Enhancement Technique (DET) to improve the boundary quality of segmentation masks. DET has two approaches, Seg-DET, which uses a segmentation network, and Edge-DET, which uses an edge-detection network. Both techniques highlight buildings, creating a better input foundation for a secondary segmentation model. Additionally, we introduce ABL(RMI), a new compounding loss consisting of Region Mutual Information Loss (RMI), Lovasz-Softmax Loss (Lovasz), and Active Boundary Loss (ABL). The combination of loss functions in ABL(RMI) is optimized to enhance and improve mask boundaries. This thesis empirically shows that DET can successfully improve segmentation boundaries, but the practical results suggest that further refinement is needed. Additionally, the results show improvements when using the new compounding loss ABL(RMI) compared to its predecessor, ABL(CE) which substitutes RMI with Cross-Entropy loss(CE)

DeNISE: Deep Networks for Improved Segmentation Edges

This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks. DeNISE utilizes the inherent differences in two sequential deep neural architectures to improve the accuracy of the predicted segmentation edge. DeNISE applies to all types of neural networks and is not trained end-to-end, allowing rapid experiments to discover which models complement each other. We test and apply DeNISE for building segmentation in aerial images. Aerial images are known for difficult conditions as they have a low resolution with optical noise, such as reflections, shadows, and visual obstructions. Overall the paper demonstrates the potential for DeNISE. Using the technique, we improve the baseline results with a building IoU of 78.9%

Contrastive Transformer: Contrastive Learning Scheme with Transformer innate Patches

This paper presents Contrastive Transformer, a contrastive learning scheme using the Transformer innate patches. Contrastive Transformer enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation. The scheme performs supervised patch-level contrastive learning, selecting the patches based on the ground truth mask, subsequently used for hard-negative and hard-positive sampling. The scheme applies to all vision-transformer architectures, is easy to implement, and introduces minimal additional memory footprint. Additionally, the scheme removes the need for huge batch sizes, as each patch is treated as an image. We apply and test Contrastive Transformer for the case of aerial image segmentation, known for low-resolution data, large class imbalance, and similar semantic classes. We perform extensive experiments to show the efficacy of the Contrastive Transformer scheme on the ISPRS Potsdam aerial image segmentation dataset. Additionally, we show the generalizability of our scheme by applying it to multiple inherently different Transformer architectures. Ultimately, the results show a consistent increase in mean IoU across all classes.Comment: 7 pages, 3 figure

MapAI: Precision in BuildingSegmentation

MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) 1 in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR)2 , the Norwegian Mapping Authority3 , AI:Hub4 , Norkart5 , and the Danish Agency for Data Supply and Infrastructure6 . The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images. Furthermore, we use IoU and Boundary IoU [1] to properly evaluate the precision of the models, with the latter being an IoU measure that evaluates the results’ boundaries. We provide the participants with a training dataset and keep a test dataset for evaluation.publishedVersio

DET: Data Enhancement Technique for Aerial Images

Deep learning and computer vision are two thriving research areas within machine learning. In recent years, as the available computing power has grown, it has led to the possibility of combining the approaches, achieving state-of-the-art results. An area of research that has greatly benefited from this development is building detection. Although the algorithms produce satisfactory results, there are still many limitations. One significant problem is the quality and edge sharpness of the segmentation masks, which are not up to the standard required by the mapping industry. The predicted mask boundaries need to be sharper and more precise to have practical use in map production. This thesis introduces a novel Data Enhancement Technique (DET) to improve the boundary quality of segmentation masks. DET has two approaches, Seg-DET, which uses a segmentation network, and Edge-DET, which uses an edge-detection network. Both techniques highlight buildings, creating a better input foundation for a secondary segmentation model. Additionally, we introduce ABL(RMI), a new compounding loss consisting of Region Mutual Information Loss (RMI), Lovasz-Softmax Loss (Lovasz), and Active Boundary Loss (ABL). The combination of loss functions in ABL(RMI) is optimized to enhance and improve mask boundaries. This thesis empirically shows that DET can successfully improve segmentation boundaries, but the practical results suggest that further refinement is needed. Additionally, the results show improvements when using the new compounding loss ABL(RMI) compared to its predecessor, ABL(CE) which substitutes RMI with Cross-Entropy loss(CE)

MapAI: Precision in Building Segmentation

MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR), the Norwegian Mapping Authority, AI:Hub, Norkart, and the Danish Agency for Data Supply and Infrastructure. The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images. Furthermore, we use IoU and Boundary IoU to properly evaluate the precision of the models, with the latter being an IoU measure that evaluates the results' boundaries. We provide the participants with a training dataset and keep a test dataset for evaluation.MapAI: Presis Bygningssegmentering er en konkurranse arrangert med Norwegian Artificial Intelligence Research Consortium (NORA) i samarbeid med Centre for Artificial Intelligence Research på Universitetet i Agder, Kartverket, AI:Hub, Norkart, og Styrelsen for Dataforsyning og Infrastruktur i Danmark. Konkurransen holdes høsten 2022. Resultatene vil bli presentert på Northern Lights Deep Learning konferansen med fokus på segmentering av bygninger ved bruk av flybilder og laserdata. Konkurransen er delt opp i to forskjellige oppgaver, hvor den første er å segmentere bygninger kun ved bruk av flybilder, mens i den andre må man bruke laserdata og kan kombinere dette med flydata. For evalueringen bruker vi IoU og Boundary IoU til å måle nøyaktigheten til modellene. Boundary IoU er en målemetode som spesielt fokuserer på kanten og formen til segmenteringsmaskene. Deltakerene får et treningsdataset, mens vi holder testdatasettet skjult til konkurransen er over