WindMill: A Parameterized and Pluggable CGRA Implemented by DIAG Design Flow

With the cross-fertilization of applications and the ever-increasing scale of models, the efficiency and productivity of hardware computing architectures have become inadequate. This inadequacy further exacerbates issues in design flexibility, design complexity, development cycle, and development costs (4-d problems) in divergent scenarios. To address these challenges, this paper proposed a flexible design flow called DIAG based on plugin techniques. The proposed flow guides hardware development through four layers: definition(D), implementation(I), application(A), and generation(G). Furthermore, a versatile CGRA generator called WindMill is implemented, allowing for agile generation of customized hardware accelerators based on specific application demands. Applications and algorithm tasks from three aspects is experimented. In the case of reinforcement learning algorithm, a significant performance improvement of $2.3\times$ compared to GPU is achieved.Comment: 7 pages, 10 figure

Mapping Irregular Local Climate Zones from Sentinel-2 Images Using Deep Learning with Sequential Virtual Scenes

Recently, the local climate zone (LCZ) system has been presented to establish the connection between urban landscape and local thermal environment. However, LCZ entities are very difficult to be identified by pixel-based classifiers or object-oriented image analysis, as they are often a complicated combination of multiple ground objects (e.g., buildings, roads, grassland, etc.). Scene classifiers, especially deep learning methods can exploit the structure or contextual information of image scenes and then improve the performance of LCZ classification. However, the square and uniform-sized image patches often bring about extra challenges, as they cannot exactly match LCZ entities of diverse sizes and shapes in most cases. In this study, a sequential virtual scene method is presented to identify LCZ entities of diverse shapes and sizes, which consists of a small “core patch” for scanning diverse entities and sequential virtual scenes for providing abundant context. Specifically, the Bidirectional Long Short-Term Memory (Bi-LSTM) were used to learn the spatial relationship among virtual scenes, respectively. Importantly, a “self-attention” mechanism is designed to weigh the contribution of every virtual scene for alleviating the influences of mixed patches, according to the similarity between its hidden state and the final hidden state. Experiments prove SVS achieves better accuracies than random forest and ResNet and has the outstanding capacity of identifying irregular LCZ entities. It is a promising way to carry out LCZ mapping in cities of different types due to its flexibility and adaptability

Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model

Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples’ lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images

Comparative Evaluation of State-of-the-Art Semantic Segmentation Networks for Long-Term Landslide Map Production

The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of various machine-learning methods, deep-learning (DL) models, and ensemble strategies in landslide detection. While transfer learning is considered an effective approach to tackle this challenge, there has been limited exploration and comparison of the temporal transferability of state-of-the-art deep-learning models in the context of LAM production, leaving a significant gap in the research. In this study, an extensive series of tests was conducted to evaluate the temporal transferability of typical semantic segmentation models, specifically U-Net, U-Net 3+, and TransU-Net, using a 10-year landslide-inventory dataset located near the epicenter of the Wenchuan earthquake. The experiment results disclose the feasibility and limitations of implementing transfer-learning methods for LAM production, particularly when leveraging the power of U-Net 3+. Furthermore, following an assessment of the effects of varying data volumes, patch sizes, and time intervals, this study recommends appropriate settings for LAM production, emphasizing the balance between efficiency and production performance. The findings from this study can serve as a valuable reference for devising an efficient and reliable strategy for large-scale LAM production in landslide-prone regions

Isotope constraints on particulate nitrogen source and dynamics in the upper water column of the oligotrophic South China Sea

Particulate nitrogen (PN) dynamics in the oligotrophic northern South China Sea (around the SouthEast Asian Time-series Study (SEATS) station) was explored by examining the isotopic compositions of suspended PN in the top 200 m over 3 years and sinking PN collected by sediment traps. The PN inventory (IPN) in the upper 100 m is larger than in the lower 100 m, exhibiting stronger seasonality. Both layers reveal significant seasonality in mean delta N-15(PN), yet, the mean in the upper 100 m (2.0 to 5.3 parts per thousand) is consistently smaller than that in the lower 100 m, implying the occurrence of vertical biological fractionation and/or an addition of N-15-depleted N from the atmosphere. The delta N-15(PN) surges in winter, when the mixed layer is deeper, indicate an intensified nitrate supply from thermocline, during which relatively stronger downward transfer efficiency was inferred by a small IPN gradient. The largest vertical gradient in IPN appeared during intermonsoon periods, corresponding with weak vertical mixing, low delta N-15(PN), and high N* values. N fixation is likely the cause for the intermonsoon delta N-15(PN) lows. The delta N-15(PN) values of trapped material at 374 m and 447 m range from 3.3 to 7.3 parts per thousand with a flux-weighted mean of 5.6 parts per thousand resembling the delta(NO3)-N-15 of upwelled sources. By using a mass-isotope balance model under the assumption of no atmospheric N deposition, we obtained an N fixation input of similar to 20 +/- 26 mmol N m(-2) yr(-1). This value accounts for only similar to 5-10% of the new production on an annual basis.Taiwan [NSC 98-2116-M-001-005]; Academia Sinica Thematic Program AFOBi; China (973 Program) [2009CB421200]; Introducing Talents of Discipline to Universities [B07034