Improving spatial agreement in machine learning-based landslide susceptibility mapping

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions

The-state-of-the-art of soft robotics to assist mobility: a review of physiotherapist and patient identified limitations of current lower-limb exoskeletons and the potential soft-robotic solutions

Background: Soft, wearable, powered exoskeletons are novel devices that may assist rehabilitation, allowing users to walk further or carry out activities of daily living. However, soft robotic exoskeletons, and the more commonly used rigid exoskeletons, are not widely adopted clinically. The available evidence highlights a disconnect between the needs of exoskeleton users and the engineers designing devices. This review aimed to explore the literature on physiotherapist and patient perspectives of the longer-standing, and therefore greater evidenced, rigid exoskeleton limitations. It then offered potential solutions to these limitations, including soft robotics, from an engineering standpoint. Methods: A state-of-the-art review was carried out which included both qualitative and quantitative research papers regarding patient and/or physiotherapist perspectives of rigid exoskeletons. Papers were themed and themes formed the review’s framework. Results: Six main themes regarding the limitations of soft exoskeletons were important to physiotherapists and patients: safety; a one-size-fits approach; ease of device use; weight and placement of device; cost of device; and, specific to patients only, appearance of the device. Potential soft-robotics solutions to address these limitations were offered, including compliant actuators, sensors, suit attachments fitting to user’s body, and the use of control algorithms. Conclusions: It is evident that current exoskeletons are not meeting the needs of their users. Solutions to the limitations offered may inform device development. However, the solutions are not infallible and thus further research and development is required

Creating a Sense of Presence in Remote Relationships : A concept of Calm Ambient artifact

Loneliness is a growing social problem that affects people from different age groups. Studies have shown that loneliness is prevalent more in young adults and the elderly demographic. Loneliness can pose serious health issues like cognitive malfunction, heart disease, stroke, depression, etc. People who stay alone from friends and family tend to feel lonelier. Conventional communication tools like a phone or video calls or using social media applications can help the users connect with people but also have adverse effects. As a result of this, the potential of an alternative nonverbal mode of communication needs to be explored. The research aims to understand individuals' behavior, traits, and hidden needs when it comes to loneliness. The purpose is to suggest an alternative way of communication that creates a sense of presence and ensures mental well for the people living alone and suffer from emotional loneliness. The concept of Calm and ambient technology has been explored in this thesis as an alternative means of communication. Users’ needs were gathered from eight semi-structured interviews, and two stakeholders were identified. Over forty ideas were generated from brainstorming. The ideas were sent to twenty individuals through snowballing. The response from them was analyzed and narrowed down by using concept screening and concept scoring. The final concept was a device called ‘One home lamp.’ The device uses light to show the presence of remote family members or loved ones to a person living alone. This concept product was then evaluated through ‘Mankoff’s heuristics’ to see its credibility as a calm ambient artifact

Label noise tolerance of deep semantic segmentation networks for extracting buildings in ultra-high-resolution aerial images of semi-built environments

Freely available building maps of rapidly changing built and semi-built environments may contain label noise. When temporal correspondence between images and labels does not hold, the labels may be subject to incorrectly observed building instances. For example, in most growing semi-built environments, such as the Kutupalong mega-camp in Bangladesh, labels corresponding to a past date may not be updated or might not have been properly labelled, resulting in label noise. Tagging/labelling can be done either manually (by humans) or automatically (by a machine/model). We manually label images for our stricter evaluation regime, but a trained model can automatically label images without human supervision. Our best performing model generates labels which improve F1-score by 17.2% and improve Intersection-over-Union score by 23.2%, when compared to the fidelity of commonly used noisy labels. Our stricter evaluation regime reveals interesting insights about the paradoxical behaviour of deep neural networks in conjunction to label noise