Light Detection and Ranging (LiDAR) is an active remote sensing technique that uses pulsed lasers to sense the surrounding environment. It works on the principle of Time of Travel (ToT) for acquiring highly accurate and high spatial resolution 3D information about the surrounding environment. LiDAR data possesses immense potential for (near) real-time applications (e.g., forestry, disaster, border security, etc.) Real-time applications demand quick analysis of the data and cannot wait for the entire data pertaining to the area of interest to be captured before producing useful insights. Thus, 3D LiDAR points should be processed as and when they are captured in the form of a continuous stream. Due to the lack of prior knowledge about the (near) real-time data and the underlying distribution, in this work, unsupervised stream mining approaches have been adapted for the analysis of streaming geospatial 3D LiDAR data. By applying different unsupervised data mining (clustering) algorithms on a huge set of 3D LiDAR data points, we have evaluated the quality of clustering based on different evaluation metrics.

In a traditional Machine Learning setting, it is assumed that the training and test dataset belong to the same feature space and the same distribution. For practical real-world scenarios, this is not always true. The model which is trained on the source dataset might give poor results on the target dataset (during testing/inferencing). We address this challenge using Unsupervised Domain Adaptation which deals with the situation in which the network is trained on the labeled source domain data and unlabeled data belonging to a different but related target domain and it gives improved results when tested on the target domain data. Also, generating data considering two different but related domains into account is a challenge. In this work, we address this challenge by proposing a framework for developing a cross-domain Generative Adversarial Network (GAN) with the aim of achieving unsupervised domain adaptation which applies to LiDAR data appertaining to extreme events. The dataset used for the study is the pre- and post-earthquake LiDAR (Light Detection and Ranging) dataset belonging to the Kumamoto earthquake of 2016. A comprehensive comparative analysis is conducted in this study to examine the efficacy of urban damage classification, both with and without the integration of unsupervised domain adaptation. We envisage that our framework can be used for performing urban damage classification in an efficient manner with increased accuracy.

There has been an increase in the adoption of Linked Data and subsequently representing data in the form of knowledge graphs across a wide spectrum of domains. There has also been significant interest in the remote sensing community to publish Earth Observation data in the form of Linked Data. As the geospatial Linked Data cloud on the internet grows, there arises a need for efficient methods of exploratory analysis of such information-rich geospatial knowledge graphs. Knowledge graph representation of remote sensing scenes has proved to add significant value for effective mining of implicit information in addition to seamless integration with other data sources. This work is geared towards visual exploration of semantically enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs). In this paper, we propose and implement an interactive web-based interface to visually explore and interact with RSS-KGs using Cesium. The proposed interface seeks to visualize the know ledge graph in the form of nodes and edges, mapped over the remote sensing scene consisting of different land use land cover regions and their inferred characteristics in addition to their spatial relationships with one another. It is envisaged that visualization in the form of nodes and edges would aid in visually validating the spatial relations in the knowledge graph, thus enhancing the understanding of the geospatial knowledge graph from the end user perspective. We demonstrate the efficacy of the interface through the visual exploration of an enriched geospatial knowledge graph of a remote sensing scene captured during an urban flood event.

Remote sensing lidar point cloud dataset embeds inherent 3D topological, topographical and complex geometrical information which possess immense potential in applications involving machine-understandable 3D perception. The lidar point clouds are unstructured, unlike images, and hence are challenging to process. In our work, we are exploring the possibility of deep learning-based question-answering on the lidar 3D point clouds. We are proposing a deep CNN-RNN parallel architecture to learn lidar point cloud features and word embedding from the questions and fuse them to form a feature mapping for generating answers. We have restricted our experiments for the urban domain and present preliminary results of binary question-answering (yes/no) using the urban lidar point clouds based on the perplexity, edit distance, evaluation loss, and sequence accuracy as the performance metrics. Our proposed hypothesis of lidar question-answering is the first attempt, to the best of our knowledge, and we envisage that our novel work could be a foundation in using lidar point clouds for enhanced 3D perception in an urban environment. We envisage that our proposed lidar question-answering could be extended for machine comprehension-based applications such as rendering lidar scene descriptions and content-based 3D scene retrieval.

In the early hours following the earthquake, supporting humanitarian actions like rescue operations and relief distribution is the primary objective of the rescue managers. The damage mapping can be performed using reliable data that can be obtained from high-resolution satellite imagery but obtaining satellite imagery can be challenging for some days post disaster due to revisit time. Considering the disaster response timing, Unmanned Aerial Vehicles (UAV) are used because ground transportation systems are ineffective due to road blockage. In this work, we make use of Light Detection and Ranging (LiDAR) 3D point cloud data obtained for Haiti Earthquake. The focus of our work is to develop and implement an approach for LiDAR data classification to enable Earthquake damage mapping and detection. This is obtained by running our deep learning network on NVIDIA Jetson Nano embedded supercomputing platform. This approach takes the advantage of embedded High-Performance computing and low power consumption capabilities of Jetson Nano which enhances the classification and promotes rapid response which is the key to manage post-disaster activities. Jetson Nano is a feasible option which provides a GPU architecture that is optimized for running energy-aware deep learning models and which generates the results in real or near-real time. We envisage that our work could be extended to perform near real-time classification of LiDAR point clouds in a post earthquake scenario.

With an increase in Geospatial Linked Open Data being adopted and published over the web, there is a need to develop intuitive interfaces and systems for seamless and efficient exploratory analysis of such rich heterogeneous multi-modal datasets. This work is geared towards improving the exploration process of Earth Observation (EO) Linked Data by developing a natural language interface to facilitate querying. Questions asked over Earth Observation Linked Data have an inherent spatio-temporal dimension and can be represented using GeoSPArql. This paper seeks to study and analyze the use of RNN-based neural machine translation with attention for transforming natural language questions into GeoSPArql queries. Specifically, it aims to assess the feasibility of a neural approach for identifying and mapping spatial predicates in natural language to GeoSPARQL's topology vocabulary extension including - Egenhofer and RCC8 relations. The queries can then be executed over a triple store to yield answers for the natural language questions. A dataset consisting of mappings from natural language questions to GeoSPArql queries over the Corine Land Cover(CLC) Linked Data has been created to train and validate the deep neural network. From our experiments, it is evident that neural machine translation with attention is a promising approach for the task of translating spatial predicates in natural language questions to GeoSPArql queries.

Real-time embedded vision tasks require extraction of complex geometric and morphological features from the raw 3D point cloud acquired using range scanning systems like lidar, radar etc. and depth cameras. Such applications are found in autonomous navigation, surveying, 3D mapping and localization tasks such as automatic target recognition (ATR). Typically, a dataset acquired during surveying by remote sensing lidar scanners, known as point cloud, is (1) huge in size and requires a big chunk of memory for processing at a single instance and, (2) experiences missing information due to rapid change in orientation of the sensor while scanning. In our work, we are addressing both the issues combinedly by proposing an online point cloud super-resolution approach for translating a low dimensional point cloud to a high dimensional dense point cloud by learning dictionaries in the low-dimensional subspace. We are presenting our approach for an urban road scenario by reconstructing dense point clouds of 3D objects and comparing results based on PSNR and Hausdorff distance.

Very High Resolution (VHR) remote sensing optical imagery is a huge source of information that can be utilized for earthquake damage detection and assessment. Time critical task such as performing the damage assessment, providing immediate delivery of relief assistance require immediate response; however, processing voluminous VHR imagery using highly accurate, but computationally expensive deep learning algorithms demands the High Performance Computing (HPC) power. To maximize the accuracy, deep convolution neural network (CNN) model is designed especially for the earthquake damage detection using remote sensing data and implemented using high performance GPU without compromising with the execution time. Geoeye1 VHR disaster images of the Haiti earthquake occurred in year 2010 is used for analysis. Proposed model provides good accuracy for damage detection; also significant execution speed is observed on GPU K80 High Performance Computing (HPC) platform.

In disasters, since time is of the essence, quick decision making based on actionable insights is desired. In our earlier work, we have demonstrated that the spatial relationships-based queries can play a vital role in the disaster response phase. However, we found that the utilization of spatial relationships rules (i.e. encoded spatial knowledge) via rule reasoning process do not scale well with the increased number of image regions. Most of the available Resource Description Framework (RDF) triplestores do not support rule reasoning due to the computational complexity and undecidable nature of the rule reasoning process. In this paper, we propose an alternative approach for utilizing spatial knowledge encoded in the form of spatial relationship rules. The proposed approach reformulates the spatial query by expanding it with the configuration encoded in the corresponding spatial relationship rule. The preliminary results are promising and show the applicability of the proposed approach during the time critical events such as flood disaster.

Natural disasters such as floods, earthquakes, hurricanes, etc. have a huge impact on a society-causing destruction of life and property in their wake. During disasters such as flood, it is crucial to understand the dynamics of the situation as it occurs for effective response. In this paper, we address the problem of satellite image classification for urban floods using deep learning. We propose an encoder-decoder neural network based on the Efficient Residual Factorized Convnet(ERFNet), for multi-class segmentation of urban floods from multi-spectral satellite imagery. The ERFNet architecture capitalizes on skip connections and one dimensional convolutions to achieve the best possible trade-off between accuracy and efficiency. Since time is of essence during a disaster, the choice of the ERFNet architecture on a high performance computing (HPC) platform is apt. Satellite imagery from WorldView-2 of floods in Srinagar, India during September 2014 have been used for this study. The tool `markGT' has been developed to assist end-to-end annotation of satellite imagery. The urban flood dataset used for this study has been generated using markGT. The proposed deep learning model over urban flood satellite imagery gives promising results on Nvidia Tesla K80 GPU. We envisage that the proposed model could be extended and improved for real-time classification of urban floods, thereby aiding disaster response personnel in making informed decisions.

Disasters such as earthquakes, floods, landslides etc. create great economic and social loss by destroying the balance of life and property and create chaos. In the wake of a disaster, it becomes very significant to take real-time and on-the-fly actions to minimize the effects of the event. Remote Sensing data acquired through airborne or spaceborne platforms is usually huge in size and requires huge time in generating actionable insights during the disaster scenario. In this work, we propose a two-fold analysis of the Very High Resolution (VHR) satellite imagery based on Compressive Sensing (CS) and Deep Learning. We propose employing a deep learning approach for inferencing over compressed sensing satellite imagery. We hypothesize that this could be beneficial in generating real-time actionable insights during a catastrophe. In our work, we are using the satellite imagery from GeoEye-1 of Haiti Earthquake. Our objectives are: (1) To generate CS images for 75%, 50%, and, 25% sampling on the sparse space and (2) To develop a deep learning pixel-level classification model based on the UNet architecture using the original and reconstructed images. The UNet architecture has shown promising results for pixel-level classification in the recent literature. We envisage to combine both the objectives into an end-to-end learning framework for on-board processing which we foresee would be of great significance in various applications for rapid disaster management response.