The Changing Landscape of Geospatial Information Markets

The endangered bonobo (Pan paniscus), endemic to the Democratic Republic of Congo (DRC), is threatened by hunting and habitat loss. Two recent wars and ongoing conflicts in the DRC greatly challenge conservation efforts. This chapter demonstrates how spatial data and maps are used for monitoring threats and prioritizing locations to safeguard bonobo habitat, including identifying areas of highest conservation value to bonobos and collaboratively mapping community-based natural resource management (CBNRM) zones for reducing deforestation in key corridor areas. We also highlight the development of a range-wide model that analysed a variety of biotic and abiotic variables in conjunction with bonobo nest data to map suitable habitat. Approximately 28 per cent of the range was predicted suitable; of that, about 27.5 per cent was located in official protected areas. These examples highlight the importance of employing spatial data and models to support the development of dynamic conservation strategies that will help strengthen bonobo protection. Le bonobo en voie de disparition (Pan paniscus), endémique à la République Démocratique du Congo (DRC), est menacé par la chasse et la perte de l’habitat. Deux guerres récentes et les conflits en cours dans le DRC menacent les efforts de conservation. Ici, nous montrons comment les données spatiales et les cartes sont utilisées pour surveiller les menaces et prioriser les espaces pour protéger l’habitat bonobo, inclut identifier les zones de plus haute valeur de conservation aux bonobos. En plus, la déforestation est réduite par une cartographie collaborative communale de gestion de ressources dans les zones de couloirs essentiels. Nous soulignons le développement d’un modèle de toute la gamme qui a analysé un variété de variables biotiques et abiotiques en conjonction avec les données de nid bonobo pour tracer la carte d’un habitat adéquat. Environ 28 per cent de la gamme est prédit adéquat; de cela, environ 27.5 per cent est dans une zone officiellement protégée. Ces exemples soulignent l’importance d’utiliser les données spatiales et les modèles pour soutenir le développement de stratégies de conservations dynamiques qui aideront à renforcer la protection des bonobos.

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A Tri-Band Cooled Receiver for Geodetic VLBI

Sensors ◽

10.3390/s21082662 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2662

Author(s):

José A. López-Pérez ◽

Félix Tercero-Martínez ◽

José M. Serna-Puente ◽

Beatriz Vaquero-Jiménez ◽

María Patino-Esteban ◽

...

Keyword(s):

Very Long Baseline Interferometry ◽

Low Noise ◽

Cryogenic Temperatures ◽

Geospatial Information ◽

Receiver Noise ◽

Geodetic Vlbi ◽

Long Baseline ◽

Front End ◽

X Band ◽

Santa Maria

This paper shows a simultaneous tri-band (S: 2.2–2.7 GHz, X: 7.5–9 GHz and Ka: 28–33 GHz) low-noise cryogenic receiver for geodetic Very Long Baseline Interferometry (geo-VLBI) which has been developed at Yebes Observatory laboratories in Spain. A special feature is that the whole receiver front-end is fully coolable down to cryogenic temperatures to minimize receiver noise. It was installed in the first radio telescope of the Red Atlántica de Estaciones Geodinámicas y Espaciales (RAEGE) project, which is located in Yebes Observatory, in the frame of the VLBI Global Observing System (VGOS). After this, the receiver was borrowed by the Norwegian Mapping Autorithy (NMA) for the commissioning of two VGOS radiotelescopes in Svalbard (Norway). A second identical receiver was built for the Ishioka VGOS station of the Geospatial Information Authority (GSI) of Japan, and a third one for the second RAEGE VGOS station, located in Santa María (Açores Archipelago, Portugal). The average receiver noise temperatures are 21, 23, and 25 Kelvin and the measured antenna efficiencies are 70%, 75%, and 60% in S-band, X-band, and Ka-band, respectively.

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Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060377 ◽

2021 ◽

Vol 10 (6) ◽

pp. 377

Author(s):

Chiao-Ling Kuo ◽

Ming-Hua Tsai

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Autonomous Vehicles ◽

Detection Accuracy ◽

Geospatial Information ◽

Combination Rules ◽

And Performance ◽

Road Characteristics ◽

Machine Readable ◽

Background Image

The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

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A data-driven performance dashboard for surgical dissection

Scientific Reports ◽

10.1038/s41598-021-94487-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Amir Baghdadi ◽

Sanju Lama ◽

Rahul Singh ◽

Hamidreza Hoshyarmanesh ◽

Mohammadsaleh Razmi ◽

...

Keyword(s):

Performance Monitoring ◽

Economic Consequences ◽

Skill Assessment ◽

Geospatial Information ◽

Surgical Performance ◽

Intelligence Models ◽

Artificial Intelligence Models ◽

Surgical Data ◽

Expert Surgeon ◽

The Rich

AbstractSurgical error and resulting complication have significant patient and economic consequences. Inappropriate exertion of tool-tissue force is a common variable for such error, that can be objectively monitored by sensorized tools. The rich digital output establishes a powerful skill assessment and sharing platform for surgical performance and training. Here we present SmartForceps data app incorporating an Expert Room environment for tracking and analysing the objective performance and surgical finesse through multiple interfaces specific for surgeons and data scientists. The app is enriched by incoming geospatial information, data distribution for engineered features, performance dashboard compared to expert surgeon, and interactive skill prediction and task recognition tools to develop artificial intelligence models. The study launches the concept of democratizing surgical data through a connectivity interface between surgeons with a broad and deep capability of geographic reach through mobile devices with highly interactive infographics and tools for performance monitoring, comparison, and improvement.

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