Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.

Of the many features that smart cities offer, safe and comfortable mobility of pedestrians within the built environment is of particular importance. Safe and comfortable mobility requires that the built environments of smart cities be accessible to all pedestrians, mobility abled and mobility impaired, given their various mobility needs and preferences. This, coupled with advanced technologies such as wayfinding applications, pedestrians can get assistance in finding the best pathways at different locations and times. Wayfinding applications comprise two components, a database component containing accessibility data, and appropriate algorithms that can utilize accessibility data to meet the mobility needs and preferences of all individuals. While wayfinding applications that provide accessibility on both permanent (e.g., steps) and temporary (e.g., snow) pathways are becoming available, there is a gap in current solutions. There are two elements in the gap, one is that the accessibility data used for finding accessible pathways for people with disabilities are not compliant to the widely agreed upon and available standards, another is that the accessibility data are not available in free and open platforms so that they can be used by developers to develop personalized wayfinding applications and services. To fill this gap, in this paper, we propose a new extension in CityGML with accessibility data. We demonstrate the benefits of the new extension by testing various route options within a city. These route options clearly show the differences between commonly (shortest and fastest) requested and produced pathways and accessible pathways that are feasible and preferred by people who are mobility impaired, such as wheelchair users.

Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the development of numerous algorithms. However, systematic studies reveal a dichotomy where algorithms generally excel at either detecting anomalies in specific datasets or generalizing across heterogeneous datasets (i.e., lack adaptability). A key source of this dichotomy may center on the singular and like biases frequently employed by existing algorithms. Current research lacks experimentation into how integrating insights from diverse biases might counteract problems in singularly biased approaches. Addressing this gap, we propose stacking-based ensemble learning for hyperspectral anomaly detection (SELHAD). SELHAD introduces the integration of hyperspectral anomaly detection algorithms with diverse biases (e.g., Gaussian, density, partition) into a singular ensemble learning model and learns the factor to which each bias should contribute so anomaly detection performance is optimized. Additionally, it introduces bootstrapping strategies into hyperspectral anomaly detection algorithms to further increase robustness. We focused on five representative algorithms embodying common biases in hyperspectral anomaly detection and demonstrated how they result in the previously highlighted dichotomy. Subsequently, we demonstrated how SELHAD learns the interplay between these biases, enabling their collaborative utilization. In doing so, SELHAD transcends the limitations inherent in individual biases, thereby alleviating the dichotomy and advancing toward more adaptable solutions.

Validating the contributions of new algorithms is a critical step in hyperspectral anomaly detection (HAD) research. Typically, validation involves comparing the performance of a proposed algorithm against other algorithms using a series of benchmark datasets. Despite the longstanding use of this comparison process, little attention has been paid to the characteristics of datasets and algorithms that ensure each algorithm has an equal opportunity of performing well. Characteristics of datasets and algorithms that inadvertently favor one algorithm can skew results, leading to misleading conclusions. To address this issue, this study introduces a feature-centric framework designed to assist in ensuring an unbiased comparison of HAD algorithms. The framework identifies significant correlations between datasets and algorithms by extracting distribution-related features from the datasets and statistically testing them against the algorithmic outcomes. The identified trends are then compared across datasets to ensure that all relevant trends are equally represented, thereby ensuring diversity and validating that no singular algorithm is afforded an inherent advantage. The framework was tested on five algorithms across 14 datasets. The results indicate that multiple measures of variance within the datasets are key drivers of diversity, and these measures accurately predicted algorithmic outcomes for 12 of the 14 datasets. This suggests that the identified trends effectively explain the algorithmic outcomes and highlights the importance of incorporating datasets with a diverse range of variances in comparisons of HAD algorithms.