scholarly journals WATLAS: high resolution and real-time tracking of many small birds in the Dutch Wadden Sea

2021 ◽  
Author(s):  
Allert Imre Bijleveld ◽  
Frank van Maarseveen ◽  
Bas Denissen ◽  
Anne Dekinga ◽  
Emma Penning ◽  
...  
Keyword(s):  
Real Time ◽  
Wadden Sea ◽  
Space Use ◽  
Data Retrieval ◽  
World Heritage Site ◽  
Fundamental Aspect ◽  
Large Species ◽  
Spatiotemporal Resolution ◽  
Trade Offs ◽  
In The Wild

Movement is a fundamental aspect of life and tracking wild animals under natural conditions has become central to animal behaviour, ecology, and conservation science. Data from tracked animals have provided novel scientific insights on extreme migratory journeys, mechanisms of navigation, space use, and early warning signals of environmental change. Technological advancements, and chiefly the development of GPS tags, have enabled animal tracking at high spatiotemporal resolution, yet trade-offs between sampling frequency, tag weight and data retrieval limit the use of GPS tags to relatively few individuals and large species. A new 'reverse-GPS' wildlife tracking system, called ATLAS, employs small low-cost tags, enabling simultaneous tracking of several hundred individuals at high accuracy and in real time, hence providing opportunities for studying inter-individual interactions and collective behaviour in the wild. Within an array of receiver stations, positions are calculated based on differences in tag-signal arrival times at minimally three receiver stations. Tags cost approximately 25 euro each and weigh as little as 0.6 g (without battery and coating). In this study, we introduce the Wadden Sea ATLAS system (WATLAS), implemented in the Dutch Wadden Sea, the Netherland's only natural UNESCO World Heritage Site, yet affected by a suit of anthropogenic activities, such as fisheries, mining, shipping, and sea level rise. From July 2017 to July 2021, we tracked 821 red knots, 182 sanderlings, 33 bar-tailed godwits, and 6 common terns. With four examples, we illustrate how WATLAS opens-up possibilities for studying space-use, among-individual variation in movement, and intra-specific interactions, and inter-specific (community) space use in the wild. We additionally argue that WATLAS could provide a tool for impact assessment, and thus aid nature conservation and management of the globally important Wadden Sea ecosystem.

scholarly journals Integrating Ecosystem Services and Human Demand for a New Ecosystem Management Approach: A Case Study from the Giant Panda World Heritage Site

2019 ◽  
Vol 12 (1) ◽  
pp. 295 ◽  
Author(s):  
Bin Fu ◽  
Pei Xu ◽  
Yukuan Wang ◽  
Yingman Guo
Keyword(s):  
Ecosystem Services ◽  
Ecosystem Management ◽  
Giant Panda ◽  
Supply And Demand ◽  
World Heritage ◽  
World Heritage Site ◽  
Management Approach ◽  
Successful Implementation ◽  
Heritage Site ◽  
Trade Offs

Ecological management based on the ecosystem approach promotes ecological protection and the sustainable use of natural resources. We developed a quantitative approach to identify the ecological function zones at the country-scale, through integrating supply and demand of ecosystem services. We selected the biologically diverse hotspot of Baoxing County, which forms a part of the Sichuan Giant Panda World Heritage Site, to explore the integration of ecosystem services supply and demand for ecosystem management. Specifically, we assessed the various support, provision, regulating, and cultural services as classified by the Millennium Ecosystem Assessment. We applied the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to spatially map habitat quality, water retention, and carbon sinks, and used statistical data to evaluate food products, animal husbandry, and product supply services. We then quantified the demands for these services in terms of population, protected species, hydropower, water, and land use. The relationship between areas of supply and areas of demand was discussed for each township, and the spatial variability in the supply–demand relationship was also considered. As a result, we spatially divided the county into six ecological functional areas, and the linkages between each region were comprehensively discussed. This study thus provides a detailed methodology for the successful implementation of an ecosystem management framework on a county-scale based on the spatial partitioning of supply and demand.

scholarly journals The Wadden Sea as a National Park and UNESCO World Heritage Site: Students’ Word Associations with These Two Conservation Designations

2021 ◽  
Vol 13 (14) ◽  
pp. 8006
Author(s):  
Till Schmäing ◽  
Norbert Grotjohann
Keyword(s):  
National Parks ◽  
National Park ◽  
Wadden Sea ◽  
World Heritage ◽  
World Heritage Site ◽  
Unesco World Heritage ◽  
Word Associations ◽  
Heritage Site ◽  
Unesco World Heritage Site ◽  
Significant Difference

The Wadden Sea ecosystem is unique in many respects from a biological perspective. This is one reason why it is protected by national parks in Germany and by its designation as a UNESCO World Heritage Site. In biology didactics, there are only a few studies that focus on the Wadden Sea. This work investigates students’ word associations with the two stimulus words “national park” and “UNESCO World Heritage Site”. The survey was conducted among students living directly at the Wadden Sea and among students from the inland. The analysis of the identified associations (n = 8345) was carried out within the framework of a quantitative content analysis to be able to present and discuss the results on a group level. A statistically significant difference was found between the two groups. Overall, results showed that the students made subject-related associations as well as a large number of associations to both stimulus words that could be judged as non-subject-related. In some cases, a connection with the region of residence could be found, but this was not generally the case. Even students’ immediate residential proximity to the Wadden Sea is no guarantee that they have knowledge of the two considered protection terms.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

Passthrough+

2020 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Gaurav Chaurasia ◽  
Arthur Nieuwoudt ◽  
Alexandru-Eugen Ichim ◽  
Richard Szeliski ◽  
Alexander Sorkine-Hornung
Keyword(s):  
Real Time ◽  
Video Encoding ◽  
View Synthesis ◽  
Performance Constraints ◽  
Trade Offs ◽  
Stereoscopic View ◽  
Target User ◽  
Spatio Temporal ◽  
And Performance ◽  
3D Scene

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.

scholarly journals A Set of Integral Grid-Coding Algebraic Operations Based on GeoSOT-3D

2021 ◽  
Vol 10 (7) ◽  
pp. 489
Author(s):  
Kaihua Hou ◽  
Chengqi Cheng ◽  
Bo Chen ◽  
Chi Zhang ◽  
Liesong He ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Information ◽  
Data Retrieval ◽  
Geospatial Data ◽  
Algebraic Operation ◽  
Real Time Processing ◽  
C Language ◽  
Time Processing ◽  
Binary Operations ◽  
Set Operations

As the amount of collected spatial information (2D/3D) increases, the real-time processing of these massive data is among the urgent issues that need to be dealt with. Discretizing the physical earth into a digital gridded earth and assigning an integral computable code to each grid has become an effective way to accelerate real-time processing. Researchers have proposed optimization algorithms for spatial calculations in specific scenarios. However, a complete set of algorithms for real-time processing using grid coding is still lacking. To address this issue, a carefully designed, integral grid-coding algebraic operation framework for GeoSOT-3D (a multilayer latitude and longitude grid model) is proposed. By converting traditional floating-point calculations based on latitude and longitude into binary operations, the complexity of the algorithm is greatly reduced. We then present the detailed algorithms that were designed, including basic operations, vector operations, code conversion operations, spatial operations, metric operations, topological relation operations, and set operations. To verify the feasibility and efficiency of the above algorithms, we developed an experimental platform using C++ language (including major algorithms, and more algorithms may be expanded in the future). Then, we generated random data and conducted experiments. The experimental results show that the computing framework is feasible and can significantly improve the efficiency of spatial processing. The algebraic operation framework is expected to support large geospatial data retrieval and analysis, and experience a revival, on top of parallel and distributed computing, in an era of large geospatial data.

scholarly journals Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Filippo Aleotti ◽  
Giulio Zaccaroni ◽  
Luca Bartolomei ◽  
Matteo Poggi ◽  
Fabio Tosi ◽  
...  
Keyword(s):  
Real Time ◽  
Depth Perception ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Handheld Devices ◽  
Single Image ◽  
Handheld Device ◽  
Time Performance ◽  
In The Wild

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.

XERIS/APEX

2021 ◽  
Vol 40 (2) ◽  
pp. 65-69
Author(s):  
Richard Wai
Keyword(s):  
Distributed Systems ◽  
Real Time ◽  
Distributed System ◽  
Dynamic Scaling ◽  
Distributed Application ◽  
Heavy Weight ◽  
System Models ◽  
In The Wild ◽  
On Line ◽  
Language Technologies

Modern day cloud native applications have become broadly representative of distributed systems in the wild. However, unlike traditional distributed system models with conceptually static designs, cloud-native systems emphasize dynamic scaling and on-line iteration (CI/CD). Cloud-native systems tend to be architected around a networked collection of distinct programs ("microservices") that can be added, removed, and updated in real-time. Typically, distinct containerized programs constitute individual microservices that then communicate among the larger distributed application through heavy-weight protocols. Common communication stacks exchange JSON or XML objects over HTTP, via TCP/TLS, and incur significant overhead, particularly when using small size message sizes. Additionally, interpreted/JIT/VM-based languages such as Javascript (NodeJS/Deno), Java, and Python are dominant in modern microservice programs. These language technologies, along with the high-overhead messaging, can impose superlinear cost increases (hardware demands) on scale-out, particularly towards hyperscale and/or with latency-sensitive workloads.

Enabling Real-time Sign Language Translation on Mobile Platforms with On-board Depth Cameras

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
HyeonJung Park ◽  
Youngki Lee ◽  
JeongGil Ko
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Data Augmentation ◽  
Language Translation ◽  
Mobile Platforms ◽  
Depth Cameras ◽  
Language Data ◽  
In The Wild ◽  
Environmental Robustness ◽  
Cloud Servers

In this work we present SUGO, a depth video-based system for translating sign language to text using a smartphone's front camera. While exploiting depth-only videos offer benefits such as being less privacy-invasive compared to using RGB videos, it introduces new challenges which include dealing with low video resolutions and the sensors' sensitiveness towards user motion. We overcome these challenges by diversifying our sign language video dataset to be robust to various usage scenarios via data augmentation and design a set of schemes to emphasize human gestures from the input images for effective sign detection. The inference engine of SUGO is based on a 3-dimensional convolutional neural network (3DCNN) to classify a sequence of video frames as a pre-trained word. Furthermore, the overall operations are designed to be light-weight so that sign language translation takes place in real-time using only the resources available on a smartphone, with no help from cloud servers nor external sensing components. Specifically, to train and test SUGO, we collect sign language data from 20 individuals for 50 Korean Sign Language words, summing up to a dataset of ~5,000 sign gestures and collect additional in-the-wild data to evaluate the performance of SUGO in real-world usage scenarios with different lighting conditions and daily activities. Comprehensively, our extensive evaluations show that SUGO can properly classify sign words with an accuracy of up to 91% and also suggest that the system is suitable (in terms of resource usage, latency, and environmental robustness) to enable a fully mobile solution for sign language translation.

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