The High Mountain Asia (HMA) continues to witness an increased frequency of glacial lake outburst floods (GLOFs), which is likely in response to continued global warming. In situ measurements to understand the triggers all across the region will remain inadequate given the vastness and lack of accessibility of the region. This work explores a data-driven logistic regression-based framework to evaluate potential GLOF triggers, such as the lake dam type, its surface area, aspect, distance, freeboard, slope, precipitation, and temperature. A comprehensive inventory of past GLOF events in the region has been compiled, with 25 events verified using pre-& post-event multispectral images acquired between 2016 and 2022. The logistic regression model is developed using samples of the positive class (lakes with confirmed GLOFs) and of the negative class (potentially dangerous lakes that have not experienced a GLOF event). The samples of the negative lake class were collected with resembling characteristics from the nearby areas of the positive class. We randomly keep 80% of the samples for training. The models performance is assessed using adjusted R2and the Akaike Information Criterion (AIC) on the test samples, which are 79% and 21.5, respectively. The classification accuracy is 90%, which is promising. In short, the proposed method is a useful tool to investigate risk of outburst flooding of glacial lakes.

Detecting firearms and accurately localizing individuals carrying them in images or videos is of paramount importance in security, surveillance, and content customization. However, this task presents significant challenges in complex environments due to clutter and the diverse shapes of firearms. To address this problem, we propose a novel approach that leverages human–firearm interaction information, which provides valuable clues for localizing firearm carriers. Our approach incorporates an attention mechanism that effectively distinguishes humans and firearms from the background by focusing on relevant areas. Additionally, we introduce a saliency-driven locality-preserving constraint to learn essential features while preserving foreground information in the input image. By combining these components, our approach achieves exceptional results on a newly proposed dataset. To handle inputs of varying sizes, we pass paired human–firearm instances with attention masks as channels through a deep network for feature computation, utilizing an adaptive average pooling (AAP) layer. We extensively evaluate our approach against existing methods in human–object interaction (HOI) detection and achieve significant results (AP = 77.8%) compared to the baseline approach (AP = 63.1%). This demonstrates the effectiveness of leveraging attention mechanisms and saliency-driven locality preservation for accurate human–firearm interaction detection. Our findings contribute to advancing the fields of security and surveillance, enabling more efficient firearm localization and identification in diverse scenarios.

A glacial lake outburst flood (GLOF) is typically a natural phenomenon caused by rapid discharge of water from a glacier, leading to a flood. The frequency of GLOFs has increased significantly in the northern areas of Pakistan, which demands identification and continuous monitoring of potentially dangerous glacial lakes. In this paper, an up-to-date inventory of glacial lakes in this region is presented. This inventory (HKH-PK-2020) has been prepared using high resolution PlanetScope imagery acquired in 2020 over northern Pakistan. It contains a total of 8808 lakes. We compare our database with the High Mountain Asia (HMA) glacial lakes inventory over northern Pakistan, prepared in 2018 using Landsat imagery. The new inventory contains 6537 more glacial lakes than the HMA inventory. Furthermore, we have prepared an annotated dataset containing 3525 images (of high resolution PlanetScope imagery over a selected number of lakes from the inventory). Each image comprises 4 bands, namely red, green, blue, and near infrared. The annotations are binary: lake or background. Finally, we have performed an ablation study with two encoder-decoder based convolutional neural networks (CNNs) trained on this dataset for pixel-based classification. Our results show an intersection over union (IoU) score of 72.81% for the lake class, which is a promising first result indicating a use of deep learning for automated inventory updates in future.

Glacial lake outburst floods (GLOFs) are a major threat to the local communities and important infrastructures in the high mountain regions. This paper focuses on the development of a benchmark dataset for glacial lakes classification in Sentinel 2 multi-spectral data and subsequent detection of glacial lakes prior to a glacial lake outburst flood (GLOF). Towards this end, we collected Sentinel 2 true color scenes of High-Mountain Asia (HMA) region using glacial lakes inventory of this region. It covers an area of 2080.12 km 2 with nearly 30,121 glacial lakes. After data collection, we retained 1200 cloud free true color images and manually generated their ground truth masks. The dataset covers lakes with different shapes, sizes and radiometric signatures. For detection of glacial lakes, we used an encoder-decoder based convolutional neural network (CNN). The model is trained on the labelled dataset of glacial lakes for semantic segmentation of true color images into two relevant classes: lake and no lake. The performance of the proposed model is evaluated using intersection over union (IoU) score. It classifies glacial lakes correctly with an IoU score of 79.90%, which is quite good as far as complexity of the problem is concerned.

Oil spills cause a significant threat to marine and coastal ecosystems. It is one of the major causes of water pollution. This research focuses on the use of deep learning for oil spills detection and classification. UNet is a convolutional neural network, originally proposed for biomedical image segmentation and modified for the discrimination of oil spills and look-alikes. The model is trained on a publicly available benchmark oil spill detection dataset of Sentinel-1 synthetic aperture radar (SAR) images. The images have been semantically segmented into multiple regions of interest such as sea surface, oil spills, look-alikes, ships and land. The proposed UNet-based model achieves intersection over union (IoU) value of 95.69% for sea surface, 60.85% for oil spills, 54.90% for look-alikes, 70.27% for ships and 96.79% for land class. The mean intersection over union (mIoU) value for all the classes is 75.70% which consitutes a nearly 10% increase compared to state of the art for this dataset.

Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to address this problem, by defining human-object interaction (and non-interaction) bounding boxes. In a given image, human and firearms are separately detected. Each detected human is paired with each detected firearm, allowing us to create a paired bounding box that contains both object and the human. A network is trained to classify these paired-bounding-boxes into human carrying the identified firearm or not. Extensive experiments were performed to evaluate the effectiveness of the algorithm, including exploiting full pose of the human, hand-keypoints, and their association with the firearm. The knowledge of spatially localized features is key to the success of our method by using multi-size proposals with adaptive average pooling. We have also extended a previously existing firearm detection dataset, by adding more images and tagging in the extended dataset the human-firearm pairs (including bounding boxes for firearms and gunmen). The experimental results $({78.5 AP}_{hold})$ demonstrate effectiveness of the proposed method.