Exploring the psychiatric symptoms among people residing at flood affected areas of Kodagu district, Karnataka

Climate Change and Mental Health: A Review of Empirical Evidence, Mechanisms and Implications

Anthropogenic climate change is an existential threat whose influences continue to increase in severity. It is pivotal to understand the implications of climate change and their effects on mental health. This integrative review aims to summarize the relevant evidence examining the harm climate change may have on mental health, suggest potential mechanisms and discuss implications. Empirical evidence has begun to indicate that negative mental health outcomes are a relevant and notable consequence of climate change. Specifically, these negative outcomes range from increased rates of psychiatric diagnoses such as depression, anxiety and post-traumatic stress disorder to higher measures of suicide, aggression and crime. Potential mechanisms are thought to include neuroinflammatory responses to stress, maladaptive serotonergic receptors and detrimental effects on one's own physical health, as well as the community wellbeing. While climate change and mental health are salient areas of research, the evidence examining an association is limited. Therefore, further work should be conducted to delineate exact pathways of action to explain the mediators and mechanisms of the interaction between climate change and mental health.

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and extract the abstract and high-level features without using training data. To assess the performance of the proposed approach, we used Sentinel-2 imagery and a digital elevation model (DEM) to map landslides in three different case studies in India, China, and Taiwan. Using minimum noise fraction (MNF) transformation, we reduced the multispectral dimension to three features containing more than 80% of scene information. Next, these features were stacked with slope data and NDVI as inputs to the CAE model. The Huber reconstruction loss was used to evaluate the inputs. We achieved reconstruction losses ranging from 0.10 to 0.147 for the MNF features, slope, and NDVI stack for all three study areas. The mini-batch K-means clustering method was used to cluster the features into two to five classes. To evaluate the impact of deep features on landslide detection, we first clustered a stack of MNF features, slope, and NDVI, then the same ones plus with the deep features. For all cases, clustering based on deep features provided the highest precision, recall, F1-score, and mean intersection over the union in landslide detection.