Matthew Danish

Matthew Danish, SM Labib, Britta Ricker et al.

Street View Imagery (SVI) is a valuable data source for studies (e.g., environmental assessments, green space identification or land cover classification). While commercial SVI is available, such providers commonly restrict copying or reuse in ways necessary for research. Open SVI datasets are readily available from less restrictive sources, such as Mapillary, but due to the heterogeneity of the images, these require substantial preprocessing, filtering, and careful quality checks. We present an efficient method for automated downloading, processing, cropping, and filtering open SVI, to be used in a survey of human perceptions of the streets portrayed in these images. We demonstrate our open-source reusable SVI preparation and smartphone-friendly perception-survey software with Amsterdam (Netherlands) as the case study. Using a citizen science approach, we collected from 331 people 22,637 ratings about their perceptions for various criteria. We have published our software in a public repository for future re-use and reproducibility.

Marco Helbich, Matthew Danish, SM Labib et al.

Computer vision-based analysis of street view imagery has transformative impacts on environmental assessments. Interactive web services, particularly Google Street View, play an ever-important role in making imagery data ubiquitous. Despite the technical ease of harnessing millions of Google Street View images, this article questions the current practices in using this proprietary data source from a European viewpoint. Our concern lies with Google's terms of service, which restrict bulk image downloads and the generation of street view image-based indices. To reconcile the challenge of advancing society through groundbreaking research while maintaining data license agreements and legal integrity, we believe it is crucial to 1) include an author's statement on using proprietary street view data and the directives it entails, 2) negotiate academic-specific license to democratize Google Street View data access, and 3) adhere to open data principles and utilize open image sources for future research.

Justas Brazauskas, Rohit Verma, Vadim Safronov et al.

Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such data means that outputs from such BIM systems typically lack the timeliness necessary for enacting decisions as a result of patterns emerging in the sensor data. Similarly, as sensors are increasingly deployed in buildings, antiquated Building Management Systems (BMSs) struggle to maintain functionality as interoperability challenges increase. In combination these motivate the need to fill an important gap in smart buildings research, to enable faster adoption of these technologies, by combining BIM, BMS and sensor data. This paper describes the data architecture of the Adaptive City Platform, designed to address these combined requirements by enabling integrated BIM and real-time sensor data analysis across both time and space.