TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices

Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged as a promising approach for running machine learning models on these devices, but integrating multiple data modalities into tinyML models still remains a challenge due to increased complexity, latency, and power consumption. This paper proposes TinyVQA, a novel multimodal deep neural network for visual question answering tasks that can be deployed on resource-constrained tinyML hardware. TinyVQA leverages a supervised attention-based model to learn how to answer questions about images using both vision and language modalities. Distilled knowledge from the supervised attention-based VQA model trains the memory aware compact TinyVQA model and low bit-width quantization technique is employed to further compress the model for deployment on tinyML devices. The TinyVQA model was evaluated on the FloodNet dataset, which is used for post-disaster damage assessment. The compact model achieved an accuracy of 79.5%, demonstrating the effectiveness of TinyVQA for real-world applications. Additionally, the model was deployed on a Crazyflie 2.0 drone, equipped with an AI deck and GAP8 microprocessor. The TinyVQA model achieved low latencies of 56 ms and consumes 693 mW power while deployed on the tiny drone, showcasing its suitability for resource-constrained embedded systems.

Graph Neural Networks as Fast and High-fidelity Emulators for Finite-Element Ice Sheet Modeling

Although the finite element approach of the Ice-sheet and Sea-level System Model (ISSM) solves ice dynamics problems governed by Stokes equations quickly and accurately, such numerical modeling requires intensive computation on central processing units (CPU). In this study, we develop graph neural networks (GNN) as fast surrogate models to preserve the finite element structure of ISSM. Using the 20-year transient simulations in the Pine Island Glacier (PIG), we train and test three GNNs: graph convolutional network (GCN), graph attention network (GAT), and equivariant graph convolutional network (EGCN). These GNNs reproduce ice thickness and velocity with better accuracy than the classic convolutional neural network (CNN) and multi-layer perception (MLP). In particular, GNNs successfully capture the ice mass loss and acceleration induced by higher basal melting rates in the PIG. When our GNN emulators are implemented on graphic processing units (GPUs), they show up to 50 times faster computational time than the CPU-based ISSM simulation.

Multi-task Deep Convolutional Network to Predict Sea Ice Concentration and Drift in the Arctic Ocean

Forecasting sea ice concentration (SIC) and sea ice drift (SID) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming climate. Given that physical sea ice models require high computational costs with complex parameterization, deep learning techniques can effectively replace the physical model and improve the performance of sea ice prediction. This study proposes a novel multi-task fully conventional network architecture named hierarchical information-sharing U-net (HIS-Unet) to predict daily SIC and SID. Instead of learning SIC and SID separately at each branch, we allow the SIC and SID layers to share their information and assist each other's prediction through the weighting attention modules (WAMs). Consequently, our HIS-Unet outperforms other statistical approaches, sea ice physical models, and neural networks without such information-sharing units. The improvement of HIS-Unet is obvious both for SIC and SID prediction when and where sea ice conditions change seasonally, which implies that the information sharing through WAMs allows the model to learn the sudden changes of SIC and SID. The weight values of the WAMs imply that SIC information plays a more critical role in SID prediction, compared to that of SID information in SIC prediction, and information sharing is more active in sea ice edges (seasonal sea ice) than in the central Arctic (multi-year sea ice).

Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Firn Layers in Radar Echograms

Echograms created from airborne radar sensors capture the profile of firn layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate the snow accumulation rates, which are required to investigate the contribution of polar ice cap melt to sea level rise. However, automatically processing the radar echograms to detect the underlying firn layers is a challenging problem. In our work, we develop wavelet-based multi-scale deep learning architectures for these radar echograms to improve firn layer detection. We show that wavelet based architectures improve the optimal dataset scale (ODS) and optimal image scale (OIS) F-scores by 3.99% and 3.7%, respectively, over the non-wavelet architecture. Further, our proposed Skip-WaveNet architecture generates new wavelets in each iteration, achieves higher generalizability as compared to state-of-the-art firn layer detection networks, and estimates layer depths with a mean absolute error of 3.31 pixels and 94.3% average precision. Such a network can be used by scientists to trace firn layers, calculate the annual snow accumulation rates, estimate the resulting surface mass balance of the ice sheet, and help project global sea level rise.

Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks

As we deal with the effects of climate change and the increase of global atmospheric temperatures, the accurate tracking and prediction of ice layers within polar ice sheets grows in importance. Studying these ice layers reveals climate trends, how snowfall has changed over time, and the trajectory of future climate and precipitation. In this paper, we propose a machine learning model that uses adaptive, recurrent graph convolutional networks to, when given the amount of snow accumulation in recent years gathered through airborne radar data, predict historic snow accumulation by way of the thickness of deep ice layers. We found that our model performs better and with greater consistency than our previous model as well as equivalent non-temporal, non-geometric, and non-adaptive models.

Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach

The precise tracking and prediction of polar ice layers can unveil historic trends in snow accumulation. In recent years, airborne radar sensors, such as the Snow Radar, have been shown to be able to measure these internal ice layers over large areas with a fine vertical resolution. In our previous work, we found that temporal graph convolutional networks perform reasonably well in predicting future snow accumulation when given temporal graphs containing deep ice layer thickness. In this work, we experiment with a graph attention network-based model and used it to predict more annual snow accumulation data points with fewer input data points on a larger dataset. We found that these large changes only very slightly negatively impacted performance.

Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region

For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e.g., estimating the ice layer thickness, predicting the ice accumulation for upcoming years, etc.). However, a vision-based conversational deep learning approach has not been explored yet, where scientists can get information by asking questions about images. In this paper, we have introduced the task of Visual Question Answering (VQA) on remote-sensed ice sheet imagery. To study, we have presented a unique VQA dataset, Polar-VQA, in this study. All the images in this dataset were collected using four types of airborne radars. The main objective of this research is to highlight the importance of VQA in the context of ice sheet research and conduct a baseline study of existing VQA approaches on Polar-VQA dataset.

Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar

The accurate prediction and estimation of annual snow accumulation has grown in importance as we deal with the effects of climate change and the increase of global atmospheric temperatures. Airborne radar sensors, such as the Snow Radar, are able to measure accumulation rate patterns at a large-scale and monitor the effects of ongoing climate change on Greenland's precipitation and run-off. The Snow Radar's use of an ultra-wide bandwidth enables a fine vertical resolution that helps in capturing internal ice layers. Given the amount of snow accumulation in previous years using the radar data, in this paper, we propose a machine learning model based on recurrent graph convolutional networks to predict the snow accumulation in recent consecutive years at a certain location. We found that the model performs better and with more consistency than equivalent nongeometric and nontemporal models.

RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

Due to climate change, we can observe a recent surge of natural disasters all around the world. These disasters are causing disastrous impact on both nature and human lives. Economic losses are getting greater due to the hurricanes. Quick and prompt response of the rescue teams are crucial in saving human lives and reducing economic cost. Deep learning based computer vision techniques can help in scene understanding, and help rescue teams with precise damage assessment. Semantic segmentation, an active research area in computer vision, can put labels to each pixel of an image, and therefore can be a valuable arsenal in the effort of reducing the impacts of hurricanes. Unfortunately, available datasets for natural disaster damage assessment lack detailed annotation of the affected areas, and therefore do not support the deep learning models in total damage assessment. To this end, we introduce the RescueNet, a high resolution post disaster dataset, for semantic segmentation to assess damages after natural disasters. The RescueNet consists of post disaster images collected after Hurricane Michael. The data is collected using Unmanned Aerial Vehicles (UAVs) from several areas impacted by the hurricane. The uniqueness of the RescueNet comes from the fact that this dataset provides high resolution post-disaster images and comprehensive annotation of each image. While most of the existing dataset offer annotation of only part of the scene, like building, road, or river, RescueNet provides pixel level annotation of all the classes including building, road, pool, tree, debris, and so on. We further analyze the usefulness of the dataset by implementing state-of-the-art segmentation models on the RescueNet. The experiments demonstrate that our dataset can be valuable in further improvement of the existing methodologies for natural disaster damage assessment.

VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis

Visual Question Answering system integrated with Unmanned Aerial Vehicle (UAV) has a lot of potentials to advance the post-disaster damage assessment purpose. Providing assistance to affected areas is highly dependent on real-time data assessment and analysis. Scope of the Visual Question Answering is to understand the scene and provide query related answer which certainly faster the recovery process after any disaster. In this work, we address the importance of \textit{visual question answering (VQA)} task for post-disaster damage assessment by presenting our recently developed VQA dataset called \textit{HurMic-VQA} collected during hurricane Michael, and comparing the performances of baseline VQA models.