CSRE

This paper presents the land cover classification capabilities of fully versus partially polarimetric SAR data for C- and L-band frequencies. Maximum Likelihood classifier with complex Wishart distribution and artificial neural network classifier (ANN) have been used for classification. The change in accuracy due to the phase information of SAR data is also assessed by comparing the classified results of intensity and complex images for all the possible polarization combinations at L- and C-band. In all the combinations, fully polarimetric data provides highest accuracy and it is not much different from that of complex partial polarimetric (HH, VV) combination. The accuracies obtained with various partial polarimetric combinations are dependent on the land cover types. Among L-, C- and X-bands, L-band offers better accuracy. By combining all bands data, accuracy improved by 7%.The accuracy has been improved slightly by combining the three components of van Zyl decomposition with the combination of X-, C-and L-band. IRS-P6 optical data over the same area has been used to compare the classification accuracy between optical and SAR data.

Mumbai Metropolitan Region (MMR) is the largest coastal city in India. The region experienced tremendous growth over the years due to rapid industrialization and urbanization. MMR is also the major center of economic activity in India. As a result there is a continuous and constant influx of population from the rest of the country. The high population density and uneven growth rate have resulted in serious environmental problems in the MMR coastal region. This paper discusses several aspects of the MMR coastal region that suffer from a wide variety of environmental as well as socio-economic problems due to unplanned and non-integrated sectoral developmental activities over the years. These problems need to be addressed in developing an Integrated Coastal Management plan for the MMR coastal region.

A novel method for calculating optimum incidence angle for the TanDEM-X system using any available digital elevation model (DEM) for the given area is proposed in this study. This method includes the plotting of slopes and aspect of the test area in a statistical way and applying mathematical approach using acquisition geometry in ascending and descending pass TanDEM-X data to optimize the incidence angle for obtaining precise DEM. Furthermore, the TanDEM-X raw DEMs in ascending and descending pass over Mumbai, India are combined using a simple weighted fusion algorithm and the quality of fused DEM thus generated is enhanced. The method adopted for fusion is just an experimental study. The problem of optimum weight selection for fusion has been addressed using height error map and a robust layover shadow mask calculated in “Integrated TanDEM-X Processor” (ITP) during TanDEM-X DEM generation. The height error map is calculated from the interferometric coherence with geometrical considerations and the robust layover and shadow map is calculated using TanDEM-X DEM and the corresponding slant range. Results show a significant reduction in the number of invalid pixels after fusion. In the fused DEM, invalids are only 2.14%, while ascending and descending pass DEMs have 5.02% and 6.34%, respectively. Statistical analysis shows a slight improvement in standard deviation of the error in fused DEM by 8% in urban area and about 5% for the whole scene. Only slight improvement in accuracy of fused DEM can be attributed to the coarse resolution of the SRTM-X DEM used as reference.

Land use and land cover change is the important factor for the urban growth analysis, and because of urban growth the city is sprawling in the haphazard manner. Because of urban growth and urban expansion there is loss of agricultural land and green area and this is directly responsible for the loss of water body. Here Ahmedabad city has taken as case to study the urban expansion and land cover change that took place in a span of 12 years from 1999 to 2009. Remote sensing methodology is adopted to study the geographical land use changes occurred during the study period. Landsat images of TM and ETM+ of Ahmedabad city area are used in this study. After image processing, supervised image classification has been performed to classify the images in to different land use categories. So knowledge about the land use and land cover is very important and useful for the urban planners and the government for preparation of future master plans and to implement these plans in the proper way.

Digital Elevation Model (DEM) is an important input for geo-spatial analysis. For various applications like flood management, ortho rectification of remote sensing images, navigation, architectural works, defence, etc., high resolution DEM is required. TanDEM-X mission was launched in 2010 to obtain high resolution global DEM with HTRI-3 standard. SAR interferometry (InSAR) technique is used for DEM generation from TanDEM-X SAR data. The accuracy of DEM depends on many parameters like height ambiguity, incidence angle, polarization, etc. In this study, time series TanDEM-X data spanning over 3 years, had processed for generating DEM at the spatial resolution of 6 m and their accuracy had studied using DGPS elevation data and SRTM 90 m DEM. The products generated during DEM generation process are DEM, precision (or height error), coherence, layover and shadow images. Using weighted average fusion technique, ascending and descending DEMs are fused for improving the quality of DEM and to reduce invalid pixels corresponding to layover and shadow areas. Results from time series data were analysed and found RMSE error of fused DEMs is in the range of 2 m to 4 m, while individual DEM has accuracy of 3 m to 6 m with respect to DGPS elevation data. Fused DEMs are having high accuracy as well as less voids. The reduction of voids by fusion, ranges from 40 to 85% in different combinations of data.

Land cover change detection is an important application of remote sensing data. Polarimetric Synthetic Aperture Radar (PolSAR) image offers increased potential to detect and monitor changes in land cover over multitemporal images. This paper explores the use of unsupervised Normalize Difference Ratio (NDR) as the change index (CI) followed by supervised thresholding to extract binary mask for changed and unchanged areas. The datasets used for this experiment are UAVSAR fully polarimetric L-band images acquired on two different dates over Hayward, California, USA. The proposed change detection technique is compared with standard supervised techniques. The result indicates that the proposed NDR technique has a higher detection rate than the traditional Differencing techniques and PCCD techniques.

Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework is proposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval.

This review paper discusses the state of the art in earth observation image (EO) information mining from a semantics based approaches. The need for mining of RS data has arisen due to the ever increasing amount of data that is being collected by various EO platforms. The EO data archives are reaching to unmanageable sizes, and the challenges in storing and disseminating this information is reaching alarming proportions. It is believed that 90% of this data remains in the archives, untouched. This is due to various reasons, such as the inability to search and find the data, the lack of contextualization in the searching and retrieving of the relevant data, the humongous amount of time required to process these datasets, etc. Currently, the data is made available to the user through interfaces that support only syntactical queries, and lack the intuitiveness which does not cater to the user's conjecture. These limiting factors highly affect the usage of these archived datasets. This review which is based on selected papers covers two areas of earth observations, which can benefit from the integration of semantic technologies: (1) data from EO imagery (2) EO data in the form of thematic data. Further, to work on huge EO image databases the computational power needs to be increased exponentially. The recent advent of graphical processing units (GPU's) for general purpose computing has tremendously helped in developing rapid approaches for the mining EO image archives. The various processes involved in using GPU computing in a variety of EO applications is discussed along with the recent work in this domain.

This paper presents a scalable object detection workflow for detecting objects, such as settlements, from remotely sensed (RS) imagery. We have successfully deployed this workflow on Titan supercomputer and utilized it for the task of mapping human settlement at a country scale. The performance of various stages in the workflow was analyzed before making it operational. The workflow implemented various strategies to address issues such as suboptimal resource utilization and long-tail effects due to unbalanced image workload, data loss due to runtime failures, and maximum wall-time constraints imposed by Titan's job scheduling policy. A mean shift clustering-based static load balancing strategy was implemented, which partitions the image load such that each partition contained similar-sized images. Furthermore, a checkpoint-restart strategy was added in the workflow as a fault-tolerance mechanism to prevent the data losses due to unforeseen runtime failures. The performance of the above-mentioned strategies was observed in various scenarios, such as node failure, exceeding wall time, and successful completion. Using this workflow, we have processed an RS data set that has a spatial resolution of 0.31 m and is comprised of 685 675 km 2 of area of the Republic of Zambia in under six hours using 5426 nodes of the Titan supercomputer.

Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reducti...

Co-occurrence of high relative humidity levels and high temperatures can increase human discomfort, thereby affecting electricity requirements for space cooling. While relative humidity is generally projected to decrease over land in a warming climate, the combined impact of warming and changes in humidity on heat stress, and thus electricity demand, are less clear. To evaluate the role of relative humidity in determining future electricity demand, we first develop predictive models based, separately, on temperature (T) and a heat stress index (apparent temperature (AT)) at an hourly scale using meteorological reanalysis data and electricity load from the United States Energy Information Administration over the four electricity regions in the southeastern United States. The AT model performs better than the T model in the historical period. We then apply the predictive models to a set of high-resolution climate projections to understand the role of relative humidity in determining the electricity demand in a warmer climate. Due to the nonlinear behavior of heat stress with warming, future electricity demand is substantially larger when estimated from AT than from T. The increase in demand projected by AT ranges between 16%-29%, 20%-33%, 14%-32% and 13%-26% and that by T model ranges between 12%-19%, 15%-19%, 14%-22% and 12%-20% over Southeast, Florida, Carolina, and Tennessee respectively. This amplification of electricity demand by humidity is strongest for the highest temperature quantiles, but also occurs at moderate future temperatures that coincide with elevated relative humidity episodes, emphasizing the importance of considering humidity in future heat stress and electricity demand assessments.

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfor...

Focal Area(s) (2) Predictive modeling through the use of AI techniques and AI-derived model components. In this white paper, we propose the design and development of a high-performance computing (HPC)-powered cyberinfrastructure to provide a computation-efficient downscaling solution that can interpolate high-resolution predictions of hydrometeorological variables (e.g., precipitation and flood depth grid) using low-resolution raster-based simulation outputs. The cyberinfrastructure adopts a theory-guided machine learning approach to enable timely and high-resolution hydrometeorological predictions for supporting decisions in hydropower operations and mitigation of multiple water-related hazards (e.g., flood, sedimentation, stream bank instability).

The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at the laboratory. This work describes the significance of each of the seven data sets and their associated challenge questions. The challenge questions for each data set were required to cover multiple difficulty levels. An international call for participation was sent to students, and researchers asking them to form teams of up to four people to apply novel data analytics techniques to these data sets.

Focal Areas (2) Predictive modeling using AI techniques and AI-derived model components. It describes the use of AI and other tools to design a prediction system comprising a hierarchy of models. Specifically, this white paper focuses on the development of a collaborative deep learning platform for evaluating and predicting spatiotemporal relationships between the hydrological and carbon cycles and includes capabilities for considering biogenic and anthropogenic inputs to these systems.

Focal Area (3) Our proposal will focus on insight gleaned from observed and simulated complex data by using artificial intelligence (AI), big data analytics, and other advanced methods. Science Challenge We will advance the understanding of water cycle extremes by evaluating the interactions between hydroclimate and land surface processes across spatiotemporal scales. Rationale A prolonged period of atmospheric, surface, or subsurface water shortage in a region is referred to as a drought. Droughts can result in the loss of agricultural productivity, ecosystem damage, water scarcity, and negative socioeconomic impacts. Therefore, the ability to estimate drought in a timely manner is crucial for emergency preparedness and planning. The state of drought in a region is determined by understanding the interaction among hydroclimate and land surface characteristics and by identifying the dominant drivers, and it is estimated by using deficit indicators (Miralles et al. 2019). Thus far, many indicators have been developed to quantify drought, and the most popular are the standardized precipitation index, Palmer drought severity index (PDSI), and standardized precipitation evapotranspiration index. The utility of these indices varies by region and applications. These indices are generally calculated at aggregate timescales with respect to a reference period with a deficit over a certain period indicating drought (Hao, Singh, and Xia 2018).