Large-scale crop mapping is essential for decision-makers to evaluate agricultural resource usage and estimate crop yields. Considering the utility of annual crop inventory (CI) statistics for monitoring crop growth, generalizing near real-time crop classification over large areas becomes necessary. Accurate crop-type identification using remote sensing data remains challenging due to the variability in crop growth patterns across time and space, the presence of crops with similar phenological stages, and the scarcity of labeled data. This study develops deep learning-based approaches to map agricultural regions at the county level using multitemporal Sentinel-1 synthetic aperture radar (SAR) data, specifically evaluating the contribution of SAR-derived input predictors for discriminating both majority and minority crops in Bei’an, Northeast China. The proposed model architecture amalgamates 1-D convolutional layers (Conv1D) with attention-based long short-term memory (LSTM) to characterize the crop types exhibiting phenological similarities using a range of SAR-derived input predictors. The results are compared with alternative multitemporal deep learning frameworks, including standalone Conv1D and Transformer models, as well as the machine learning algorithm random forest (RF), which serves as the baseline for comparison. The designed architecture (Conv1D-LSTM) achieved the highest$F1$scores (maize: 87%, soybean: 86%, and other crops: 85%) when applied to an inherently imbalanced dataset, using m-chi decomposition features as input predictors. The results provide superior performance in terms of effectiveness and efficiency compared to other selected models. The monthly in-season crop classification underscores the importance of temporal dependencies and the availability of multitemporal observations for learning dynamic growth patterns over large areas. Furthermore, the interpretation of model learning processes and outcomes is explained by visualizing weight distributions and hidden features. This study offers a comprehensive evaluation of essential SAR features in multitemporal satellite data for accurate crop mapping, utilizing advanced deep learning techniques. This work is available athttps://github.com/Niantangliu/Deep-learning-crop-mapping.

The amalgamation of data visualization and geospatial insights has driven significantly advancements in remote sensing for applications like damage detection and urban planning, particularly building rooftop extraction from high spatial satellite imagery. However, building rooftop extraction using deep learning methods often results in outputs with unclear margin delineation. In this study, we propose a novel approach that combines Transformer architectures, involution, and an enhanced U-net (E-Unet) [1] to improve building footprint extraction performance. Our method demonstrates remarkable accuracy in complex urban environments in the Waterloo Building Dataset. Transformers, renowned for their success in natural language processing, have excelled in adeptness at analyzing sequential data. By using the embedding and multi-head attention blocks, this method is becoming increasingly valuable for building extraction. Involution, in turn, augments neural networks by providing spatial-specific adaptability, effectively extracting inter-band features and surpassing convolutional limitations. Through comprehensive comparative model experiments on the Waterloo building dataset, the optimal architecture was identified. The model significantly enhances accuracy when the Transformer architecture is integrated at the output of the E-Unet. Our proposed network achieves outstanding at the crucial metric values of IoU, mIoU, Precision, F1-score in 81.2, 91.9, 92.9, 89.8 (%), surpassing established frameworks such as FCN-8s, U-Net, DeepLab v3+, Fast Statistical Convolutional Neural Network (SCNN), High-Resolution Net (HRNet) v2, Mask R-CNN, as well as E-Unet.

In this paper, the performance of freely available digital elevation models (DEM) when creating Sentinel-1 terrain-corrected γ0backscatter data for tropical forest measurements was evaluated in Google Earth Engine (GEE). The evaluation metrics considered are the radiometric and geometric quality and topographic normalization quality assessments. All the DEMs produced similar results in detecting stages of deforestation caused by mining in the study area, particularly in flatter terrains. However, the Copernicus 30m DEM is proposed as preferable for steep terrains compared to NASADEM, SRTM, and 30m ALOS World DEM.

Peatlands are important carbon sinks however many peatlands have been degraded. This lowers the water level releasing greenhouse gases into the atmosphere. Synthetic aperture radar (SAR) satellite data can be used to monitor the water level in peatlands and therefore identifies areas that are in need of restoration. Using quad-pol L-band data from ALOS-2 shows potential to identify negative change in the water level. Observing eigenvalues and eigenvectors of the change matrix of a bog in Scotland identifies a lower water table (drier peatland) that corresponds with less surface scattering. In addition, observing the coherence regions of different points around peatlands shows variations in the scattering. Improvements to these results would be made by obtaining acquisitions on dates which had a higher water table.

With each successive generation, network adapters for high-performance networks are becoming more powerful and feature rich. High-performance NICs can now provide support for performing complex group communication operations on the NIC without any host CPU involvement. Several "offloading interfaces" have been designed with the collective communications goal being the complete offloading of arbitrary communication patterns. In this work, we analyze the offloading model offered in the Portals 4 specification in detail. We perform a theoretical analysis based on abstract communication graphs and show several protocols for implementing offloaded communication schedules. Based on our analysis, we propose and implement an extension to the Portals 4 specification that enables offloading any communication pattern completely to the NIC. Our measurements with several advanced communication algorithms confirm that the enhancements provide good overlap and asynchronous progress in practical settings. Altogether, we demonstrate a complete and simple scheme for implementing arbitrary offloaded communication algorithms and hardware. Our protocols can act as a blueprint for the development of communication hardware and middleware while optimizing the whole communication stack.

Power and energy concerns are motivating chip manufacturers to consider future hybrid-core processor designs that combine a small number of traditional cores optimized for single-thread performance with a large number of simpler cores optimized for throughput performance. This trend is likely to impact the way compute resources for network protocol processing functions are allocated and managed. In particular, the performance of MPI match processing is critical to achieving high message throughput. In this paper, we analyze the ability of simple and more complex cores to perform MPI matching operations for various scenarios in order to gain insight into how MPI implementations for future hybrid-core processors should be designed.

The latest version of the Portals interconnect programming interface contains several improvements intended for MPI point-to-point and one-sided communication operations, including new functionality defined in MPI-3. This paper discusses the rationale for these improvements to Portals and describes how they can be used in an MPI implementation. We provide preliminary micro-benchmark performance results using a reference implementation of Portals over InfiniBand Verbs and the Open MPI implementation.

Achieving the next three orders of magnitude performance increase to move from petascale to exascale computing will require a significant advancements in several fundamental areas. Recent studies have outlined many of the challenges in hardware and software that will be needed. In this paper, we examine these challenges with respect to high-performance networking. We describe the repercussions of anticipated changes to computing and networking hardware and discuss the impact that alternative parallel programming models will have on the network software stack. We also present some ideas on possible approaches that address some of these challenges.

Recent trends in high performance computing have renewed interest in the ability of platforms to sustain high message throughput rates. The continued growth in platform scale, combined with emerging application areas, are pushing platforms to support increasing message rates. Best-case message throughput has grown in previous hardware generations due to growing clock rates and software optimization techniques. However, previous work has shown that MPI receive queue length and cache hit rates can drastically impact message throughput, leading to a significantly lower worst-case message throughput. This paper introduces the Sandia message throughput benchmark which measures message throughput using a communication pattern which is neither best-case nor worst-case, but which mimics communication patterns found in real-world applications. Results on InfiniBand, Myrinet, and Cray XT platforms are presented, and suggest that message rates on some networks are greatly impacted by cache invalidation between communication phases, simultaneously sending and receiving, and by communicating with more than one peer simultaneously.

Developing multi-threaded graph algorithms, even when using the MTGL infrastructure, provides a number of challenges, including discovering appropriate levels of parallelism, preventing memory hot spotting, and eliminating accidental synchronization. In this paper, we have demonstrated that using the combination of Qthreads and MTGL with commodity processors enables the development and testing of algorithms without the expense and complexity of a Cray XMT. While achievable performance is lower for both the Opteron and Niagara platform, performance issues are similar. While we believe it is possible to port Qthreads to the Cray XMT, this work is still on-going. Therefore, porting work still must be done to move algorithm implementations between commodity processors and the XMT. Although it is likely that the Qthreads-version of an algorithm will not be as optimized as a natively implemented version of the algorithm, such a performance impact may be an acceptable trade-off for ease of implementation.

The growth in the number of generally available, distributed, heterogeneous computing systems places increasing importance on the development of user-friendly tools that enable application developers to efficiently use these resources. Open MPI provides support for several aspects of heterogeneity within a single, open-source MPI implementation. Through careful abstractions, heterogeneous support maintains efficient use of uniform computational platforms. We describe Open MPI's architecture for heterogeneous network and processor support. A key design features of this implementation is the transparency to the application developer while maintaining very high levels of performance. This is demonstrated with the results of several numerical experiments

The Open MPI Project is a growing community surrounding an open source implementation of the Message Passing Interface, developed in a collaboration between research institutions, HPC vendors, and US national laboratories. Open MPI has evolved into a high performance, scalable MPI implementation, providing a highly modular architecture that not only adapts to a wide variety of environments, but also uniquely lends itself to HPC research.The meeting will consist of three parts:1. Members of the Open MPI core development team will be presenting the current status of Open MPI. 2. Discuss possible future directions for Open MPI, actively soliciting feedback from real-world MPI users and ISVs with MPI-based products. 3. Active discussion about how HPC researchers can leverage the Open MPI project for their own work, and how MPI users can obtain the best performance out of real-world applications.