scholarly journals OPERATIONAL-SCALE GEOAI FOR PAN-ARCTIC PERMAFROST FEATURE DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGERY

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 175-180
Author(s):  
M. Udawalpola ◽  
A. Hasan ◽  
A. K. Liljedahl ◽  
A. Soliman ◽  
C. Witharana
Keyword(s):  
Satellite Imagery ◽  
High Performance ◽  
Large Scale ◽  
Feature Detection ◽  
Image Interpretation ◽  
Value Added ◽  
Input Image ◽  
High Resolution Satellite Imagery ◽  
Graphical Processing ◽  
Arctic Science

Abstract. Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. The imagery is still largely underutilized, and value-added Arctic science products are rare. Knowledge discovery through artificial intelligence (AI), big imagery, high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of petabyte-scale imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. In addition to semantic complexities, multitude factors that are inherent to sub-meter resolution satellite imagery, such as file size, dimensions, spectral channels, overlaps, spatial references, and imaging conditions challenge the direct translation of AI-based approaches from computer vision applications. Memory limitations of Graphical Processing Units necessitates the partitioning of an input satellite imagery into manageable sub-arrays, followed by parallel predictions and post-processing to reconstruct the results corresponding to input image dimensions and spatial reference. We have developed a novel high performance image analysis framework –Mapping application for Arctic Permafrost Land Environment (MAPLE) that enables the integration of operational-scale GeoAI capabilities into Arctic science applications. We have designed the MAPLE workflow to become interoperable across HPC architectures while utilizing the optimal use of computing resources.

scholarly journals BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 111-116
Author(s):  
C. Witharana ◽  
M. A. E. Bhuiyan ◽  
A. K. Liljedahl
Keyword(s):  
High Performance Computing ◽  
Satellite Imagery ◽  
High Performance ◽  
Large Scale ◽  
Image Interpretation ◽  
Accurate Estimate ◽  
Neural Nets ◽  
Ground Subsidence ◽  
The Arctic ◽  
Performance Computing

Abstract. Permafrost thaw has been observed at several locations across the Arctic tundra in recent decades; however, the pan-Arctic extent and spatiotemporal dynamics of thaw remains poorly explained. Thaw-induced differential ground subsidence and dramatic microtopographic transitions, such as transformation of low-centered ice-wedge polygons (IWPs) into high-centered IWPs can be characterized using very high spatial resolution (VHSR) commercial satellite imagery. Arctic researchers demand for an accurate estimate of the distribution of IWPs and their status across the tundra domain. The entire Arctic has been imaged in 0.5 m resolution by commercial satellite sensors; however, mapping efforts are yet limited to small scales and confined to manual or semi-automated methods. Knowledge discovery through artificial intelligence (AI), big imagery, and high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of VHSR imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. We are in the process of developing an automated Mapping Application for Permafrost Land Environment (MAPLE) by combining big imagery, AI, and HPC resources. The MAPLE uses deep learning (DL) convolutional neural nets (CNNs) algorithms on HPCs to automatically map IWPs from VHSR commercial satellite imagery across large geographic domains. We trained and tasked a DLCNN semantic object instance segmentation algorithm to automatically classify IWPs from VHSR satellite imagery. Overall, our findings demonstrate the robust performances of IWP mapping algorithm in diverse tundra landscapes and lay a firm foundation for its operational-level application in repeated documentation of circumpolar permafrost disturbances.

scholarly journals Penataan Batas Wilayah Administrasi Desa, Hambatan dan Alternatif Solusi dengan Pendekatan Geospasial

1969 ◽  
Vol 12 (2) ◽  
pp. 131-147
Author(s):  
Asadi Asadi
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
High Resolution Satellite Imagery ◽  
Main Instrument ◽  
Topographical Maps ◽  
Imagery Data

Law No. 6 of 2014 concerning Villages provides additional evidence that Indonesia has paid more attention and respect to the existence of villages. The significant amount of village expansion lately is not matched with the clarity of village boundaries that may rise in to potential conflicts. Ideally, the entire instruments to structure village boundaries must first be prepared. One of the instruments needed is the availability of large scale of basic maps (topographical maps) as the main instrument of making a village map. Unfortunately, the large-scale topographical maps are not available yet. This paper provides an alternative acceleration of village boundaries arrangement using High Resolution Satellite Imagery Data that has passed orthorectified process. By involving the community and village leaders in the process of structuring boundaries, and supported by the spirit of fraternity, all problems occured during the activity of village boundaries can be solved with the very best solution.Keywords: village boundary, High Resolution Satellite Imagery Data, spirit of fraternityUndang-Undang Nomor 6 Tahun 2014 tentang Desa memberikan tambahan bukti bahwa negara semakin memperhatikan dan menghormati keberadaan desa. Adanya pemekaran wilayah desa yang signifikan akhir-akhir ini, tidak diimbangi dengan kejelasan batas wilayah desa,berpotensi menimbulkan konflik. Idealnya, seluruh instrumen untuk melakukan penataan batas wilayah desa harus terlebih dahulu disiapkan. Salah satu instrumen tersebut adalah tersedianya peta dasar (peta rupabumi) skala besar sebagai bahan utama pembuatan peta desa. Sayangnya ketersediaan peta rupabumi skala besar belum tersedia. Tulisan ini memberikan alternatif percepatan penataan batas wilayah desa yang dapat menggunakan Citra Satelit Resolusi Tinggi (CSRT) yang sudah melalui proses ortorektifikasi. Dengan melibatkan masyarakat dan tokoh masyarakat desa dalam melakukan proses penataan batas wilayah, dan dengan didukung semangat persaudaraan, diharapkan permasalahan batas wilayah desa dapat diselesaikan dengan sebaik-baiknya.Kata kunci: batas desa, metode kartometrik, CSRT, semangat persaudaraan

scholarly journals Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

2020 ◽  
Vol 12 (7) ◽  
pp. 1213 ◽  
Author(s):  
Muhammad M. Raza ◽  
Chris Harding ◽  
Matt Liebman ◽  
Leonor F. Leandro
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Sudden Death ◽  
Satellite Imagery ◽  
Large Scale ◽  
Vegetation Index ◽  
Sudden Death Syndrome ◽  
Alternative Methods ◽  
High Resolution Satellite Imagery ◽  
Soybean Diseases

Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.

Mapping of settlements in high-resolution satellite imagery using high performance computing

GeoJournal ◽  
2007 ◽  
Vol 69 (1-2) ◽  
pp. 119-129 ◽  
Author(s):  
Anil Cheriyadat ◽  
Eddie Bright ◽  
David Potere ◽  
Budhendra Bhaduri
Keyword(s):  
High Resolution ◽  
High Performance Computing ◽  
Satellite Imagery ◽  
High Performance ◽  
High Resolution Satellite Imagery ◽  
Performance Computing

scholarly journals Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco

2021 ◽  
Vol 13 (9) ◽  
pp. 1740
Author(s):  
Chenxi Lin ◽  
Zhenong Jin ◽  
David Mulla ◽  
Rahul Ghosh ◽  
Kaiyu Guan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Large Scale ◽  
Feature Space ◽  
Temporal Information ◽  
Tree Crops ◽  
Climatic Regions ◽  
Multi Temporal ◽  
Olive Trees ◽  
High Resolution Satellite Imagery

Timely and accurate monitoring of tree crop extent and productivities are necessary for informing policy-making and investments. However, except for a very few tree species (e.g., oil palms) with obvious canopy and extensive planting, most small-crown tree crops are understudied in the remote sensing domain. To conduct large-scale small-crown tree mapping, several key questions remain to be answered, such as the choice of satellite imagery with different spatial and temporal resolution and model generalizability. In this study, we use olive trees in Morocco as an example to explore the two abovementioned questions in mapping small-crown orchard trees using 0.5 m DigitalGlobe (DG) and 3 m Planet imagery and deep learning (DL) techniques. Results show that compared to DG imagery whose mean overall accuracy (OA) can reach 0.94 and 0.92 in two climatic regions, Planet imagery has limited capacity to detect olive orchards even with multi-temporal information. The temporal information of Planet only helps when enough spatial features can be captured, e.g., when olives are with large crown sizes (e.g., >3 m) and small tree spacings (e.g., <3 m). Regarding model generalizability, experiments with DG imagery show a decrease in F1 score up to 5% and OA to 4% when transferring models to new regions with distribution shift in the feature space. Findings from this study can serve as a practical reference for many other similar mapping tasks (e.g., nuts and citrus) around the world.

scholarly journals Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning

2022 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Zhihao Wei ◽  
Kebin Jia ◽  
Xiaowei Jia ◽  
Pengyu Liu ◽  
Ying Ma ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Ground Truth ◽  
Fine Tuning ◽  
Forest Monitoring ◽  
Learning Method ◽  
Ground Truth Data ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.

scholarly journals Recycling of Waste Tyre into Silica-Rubber Compounds for Green Tyre Application

2021 ◽  
Author(s):  
Joyeeta Ghosh ◽  
Sakrit Hait ◽  
Soumyajit Ghorai ◽  
Dipankar Mondal ◽  
Gert Heinrich ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Mechanical Performance ◽  
Value Added ◽  
Styrene Butadiene Rubber ◽  
Scale Production ◽  
Butadiene Rubber ◽  
Rubber Compounds ◽  
Styrene Butadiene ◽  
Devulcanized Rubber

Abstract The prevention of detrimental effects to environment, owing to generation of a huge amount of rubber wastes, is a big challenge across the globe that warrants a thorough investigation of recycling and reuses waste of rubber products. In this spirit a sustainable development of a devulcanization process along with the production of value added devulcanized rubber is a task of hours. The present work describes a simultaneous devulcanization and chemical functionalisation process of waste solution styrene butadiene rubber (S-SBR). This kind of rubber is generally used as the main polymer component in silica filled tread rubber compounds for high-performance passenger car tyres. As-grown ethoxy groups on the functionalized devulcanized styrene butadiene rubber (D-SBR) are exploited for the coupling between silica and the devulcanized rubber chains. We compare the mechanical and dynamic mechanical performance of D-SBR with that of virgin SBR control composites. Covalently bonding interfaces developed from the pendent ethoxy groups of D-SBR and silanol groups on the silica surface offer a competitive and promising performance of the D-SBR based composites. We conclude that the present approach can be further utilized for the large-scale production of different rubber products with satisfied elastomeric performance.

scholarly journals Large-scale monitoring of coppice forest clearcuts by multitemporal very high resolution satellite imagery. A case study from central Italy

2011 ◽  
Vol 115 (4) ◽  
pp. 1025-1033 ◽  
Author(s):  
Gherardo Chirici ◽  
Diego Giuliarelli ◽  
Daniele Biscontini ◽  
Daniela Tonti ◽  
Walter Mattioli ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Central Italy ◽  
High Resolution Satellite Imagery ◽  
Coppice Forest ◽  
Very High
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