Automatic Prediction of Building Age from Photographs

We present a first method for the automated age estimation of buildings from unconstrained photographs. To this end, we propose a two-stage approach that firstly learns characteristic visual patterns for different building epochs at patch-level and then globally aggregates patch-level age estimates over the building. We compile evaluation datasets from different sources and perform an detailed evaluation of our approach, its sensitivity to parameters, and the capabilities of the employed deep networks to learn characteristic visual age-related patterns. Results show that our approach is able to estimate building age at a surprisingly high level that even outperforms human evaluators and thereby sets a new performance baseline. This work represents a first step towards the automated assessment of building parameters for automated price prediction.Comment: Preprint of paper to appear in ACM International Conference on Multimedia Retrieval (ICMR) 2018 Conferenc

Visual Interpretability of Image-based Real Estate Appraisal

Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre-and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives

Civil infrastructure damage and corrosion detection: an application of machine learning

Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, and roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one to overcome the challenges and shortcomings (objectivity and reliability) associated with the manual inspection methods. Deep learning methods have been widely reported in the literature for civil infrastructure corrosion detection. Among them, convolutional neural networks (CNNs) display promising applicability for the automatic detection of image features less affected by image noises. Therefore, in the current study, we propose a modified version of deep hierarchical CNN architecture, based on 16 convolution layers and cycle generative adversarial network (CycleGAN), to predict pixel-wise segmentation in an end-to-end manner using the images of Bolte Bridge and sky rail areas in Victoria (Melbourne). The convolutedly designed model network proposed in the study is based on learning and aggregation of multi-scale and multilevel features while moving from the low convolutional layers to the high-level layers, thus reducing the consistency loss in images due to the inclusion of CycleGAN. The standard approaches only use the last convolutional layer, but our proposed architecture differs from these approaches and uses multiple layers. Moreover, we have used guided filtering and Conditional Random Fields (CRFs) methods to refine the prediction results. Additionally, the effectiveness of the proposed architecture was assessed using benchmarking data of 600 images of civil infrastructure. Overall, the results show that the deep hierarchical CNN architecture based on 16 convolution layers produced advanced performances when evaluated for different methods, including the baseline, PSPNet, DeepLab, and SegNet. Overall, the extended method displayed the Global Accuracy (GA); Class Average Accuracy (CAC); mean Intersection Of the Union (IOU); Precision (P); Recall (R); and F-score values of 0.989, 0.931, 0.878, 0.849, 0.818 and 0.833, respectively

Property Appraisal Platform

This document focuses on the internship in the company DeepNeuronic as part of the project ”Property Appraisal Platform”. This project’s main objective was to develop machine learning models capable of inferring real estate prices using machine learning models and a limited set of features capable of describing a property. In order to achieve the objective, the project was divided into two major phases. In the first phase the state of the art was studied and a dataset collection was put together with the aim of creating a comprehensive representation of the real estate market all across the globe. With this dataset collection available, a set of features was chosen according to their relevancy for the main problem. The second phase consisted of the major practical developments, such as the model creation and dataset improvements. With this in mind, the most relevant metrics were chosen and the models were evaluated in the chosen datasets, creating a set of baseline results to improve upon. Afterwards, multiple other experiments were done, tackling different areas of interest that could potentially improve upon the performance of the models. In total, four different models were evaluated and all the experiments improved upon the baseline results. As an highlight, in the last experiment we propose the transformation of the target label from the property price to the ”Coefficient of the price per square meter compared to the suburb average”. Using this new target label, the results obtained were considerably better. All of these experiments were redone in a new more complex dataset, with all of the experiments improving upon the baseline results obtained in this dataset, reinforcing the idea that these experiments can be used even in more complex datasets.Este documento foi criado no âmbito do estágio realizado na empresa DeepNeuronic como parte do projeto ”Plataforma de Avaliação de Propriedades”. O objetivo do mesmo foi desenvolver modelos de aprendizagem automática capazes de avaliar preços do mercado imobiliário usando modelos inteligentes e um conjunto limitado de características capazes de descrever uma propriedade. Para atingir este objetivo o projeto foi dividido em duas partes principais. Na primeira parte foi feito um estudo intensivo do estado da arte, e criada uma coleção de bancos de dados extensiva, representante do mercado imobiliário no mundo inteiro. Com esta coleção disponível, um conjunto de características foram escolhidas de acordo com a sua relevância para o problema em questão. A segunda fase consistiu nos desenvolvimentos práticos principais, envolvendo a criação de modelos e melhorias nos bancos de dados. Para isso foram escolhidas as métricas mais relevantes, e foram avaliados os modelos nos bancos de dados iniciais, criando assim um conjunto de resultados base. Seguidamente, múltiplas experiências foram feitas, abordando diferentes áreas de interesse que podiam potencialmente melhorar os resultados base. No total quatro modelos diferentes foram avaliados e as experiências realizadas todas melhoraram os resultados base obtidos. De especial relevância, na última experiência propomos a transformação do preço da propriedade para uma variável objetivo que pode ser descrita como o ”Coeficiente do preço por metro de área quadrado comparado à média do subúrbio”. Usando esta variável os resultados obtidos foram consideravelmente melhores, estas experiências foram refeitas em um novo banco de dados consideravelmente mais complexo, verificando-se também que todas estas experiências melhoram os resultados obtidos inicialmente, reforçando a ideia que estas experiências podem ser usadas mesmo em bancos de dados mais complexos

Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research