HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}.Comment: This work has been accepted by IEEE TGRS for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Peningkatan Performa Prediksi Daerah Potensi Penangkapan Ikan Dengan Metode Threshold Adaptif

Metode yang digunakan untuk penentuan thermal fronts adalah algoritme Single Image Edge Detection dengan threshold statis 0,5 yang didapatkan dari penelitian terdahulu. Kekurangan dari metode threshold statis adalah tingginya bias akurasi hasil deteksi dikarenakan lebih banyaknya hasil deteksi negatif tervalidasi dibandingkan deteksi front murni yang tervalidasi. Penelitian yang diusulkan bertujuan untuk meningkatkan performa metode deteksi daerah potensi ikan. Peningkatan performa deteksi thermal front dapat dilakukan dengan mencari nilai threshold optimal yang sesuai untuk masing-masing citra. Threshold adaptif didapatkan dari hasil analisis histogram pada setiap citra greyscale yang diproses. Untuk mendapatkan nilai threshold optimal dipilih Algoritme Otsu dengan pertimbangan proses cepat dan ketepatan hasil menengah. Penyesuaian metode dibutuhkan karena sifat dasar data SST yang dikonversi menjadi raster. Modifikasi metode Otsu dilakukan pada perhitungan nilai threshold optimal dengan rentang intensitas greyscale 1-254. Pemurnian front menggunakan pendekatan Geodesic Buffering dengan jarak maksimal 10 kilometer untuk mengatasi pergeseran front akibat noise suppression. Penelitian telah dilakukan dan menghasilkan metode deteksi daerah potensi ikan dengan performa recall yang lebih tinggi 25,42% dibandingkan metode threshold statis. Nilai recall lebih tinggi membuktikan bahwa metode yang diusulkan mampu menghasilkan lebih banyak hasil deteksi front murni yang lokasinya tervalidasi dengan data aktual penangkapan ikan

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

First evidence of paleoearthquakes along the Carboneras Fault Zone (SE Iberian Peninsula): Los Trances site

Seismogenic faults that have not produced historical large earthquakes remain unnoticed and, thus, are dangerously left out from seismic hazard analyses. The seismogenic nature of the Carboneras Fault Zone, a left-lateral strikeslip fault in the Eastern Betic Shear Zone (southeastern Spain), has not been fully explored to date in spite of having a morphological expression equivalent to the Alhama de Murcia Fault, a seismogenic fault in the same tectonic system. This study provides the first paleoseismic evidence of the seismogenic nature of the Carboneras Fault Zone, based on the analysis of 3 trenches at Los Trances site, on the northwestern edge of the La Serrata Range. Cross cutting relationships and numerical dating, based on radiocarbon, thermoluminescence and U-series, reveal a minimum of 4 paleoearthquakes: Paleoearthquake1 (the oldest) and Paleoearthquake2 took place after 133ka, Paleoearthquake3 occurred between 83-73ka and Paleoearthquake4 happened after 42.5ka (probably after 30.8ka), resulting in a maximum possible average recurrence of 33ka. This value, based on a minimum amount of paleoearthquakes, is probably overestimated, as it does not scale well with published slip-rates derived from offset channels or GPS geodetical data. The characterization of this fault as seismogenic, implies that it should be considered in the seismic hazard analyses of the SE Iberian Peninsula

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

First evidence of paleoearthquakes along the Carboneras Fault Zone (SE Iberian Peninsula): Los Trances site

Seismogenic faults that have not produced historical large earthquakes remain unnoticed and, thus, are dangerously left out from seismic hazard analyses. The seismogenic nature of the Carboneras Fault Zone, a left-lateral strikeslip fault in the Eastern Betic Shear Zone (southeastern Spain), has not been fully explored to date in spite of having a morphological expression equivalent to the Alhama de Murcia Fault, a seismogenic fault in the same tectonic system. This study provides the first paleoseismic evidence of the seismogenic nature of the Carboneras Fault Zone, based on the analysis of 3 trenches at Los Trances site, on the northwestern edge of the La Serrata Range. Cross cutting relationships and numerical dating, based on radiocarbon, thermoluminescence and U-series, reveal a minimum of 4 paleoearthquakes: Paleoearthquake1 (the oldest) and Paleoearthquake2 took place after 133ka, Paleoearthquake3 occurred between 83–73ka and Paleoearthquake4 happened after 42.5ka (probably after 30.8ka), resulting in a maximum possible average recurrence of 33ka. This value, based on a minimum amount of paleoearthquakes, is probably overestimated, as it does not scale well with published slip-rates derived from offset channels or GPS geodetical data. The characterization of this fault as seismogenic, implies that it should be considered in the seismic hazard analyses of the SE Iberian Peninsula