A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm DOI Creative Commons

Yougui Ren,

Zhibin Zhao,

Junjian Jiang

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(18), С. 8535 - 8535

Опубликована: Сен. 22, 2024

With the rapid development of remote sensing image data, efficient retrieval target images interest has become an important issue in various applications including computer vision and sensing. This research addressed low-accuracy problem traditional content-based algorithms, which largely rely on comparing entire features without capturing sufficient semantic information. We proposed a scene graph similarity-based algorithm. Firstly, one-shot object detection algorithm was designed for based Siamese networks tailored to objects unknown class query image. Secondly, construction developed, their attributes spatial relationships. Several strategies were different relationships, full connections, random nearest star or ring connections. Thirdly, by making use edge feature extraction, extraction network established features. Fourthly, neural tensor network-based similarity calculation vectors obtain results. Fifthly, dataset named with graphs (RSSG) built testing, contained 929 corresponding generated developed strategies. Finally, through performance comparison experiments algorithms AMFMN, MiLaN, AHCL, precision rates, Precision@1 improved 10%, 7.2%, 5.2%, Precision@5 3%, 5%, 1.7%; Precision@10 1.7%, 0.6%. In recall Recall@1 2.5%, 4.3%, 1.3%; Recall@5 3.7%, 6.2%, 2.1%; Recall@10 4.4%, 7.7% 1.6%.

Язык: Английский

Community scientists provide knowledge and public education and help enforce environmental regulations in social-ecological systems DOI Creative Commons
Ryan O’Connor, Giulio A. De Leo, Nicole M. Ardoin

и другие.

Communications Earth & Environment, Год журнала: 2025, Номер 6(1)

Опубликована: Фев. 8, 2025

Язык: Английский

Процитировано

1
9. Exploring how place connections support sustainability solutions in marine socio-ecological systems DOI Creative Commons
Nicole M. Ardoin, Ryan O’Connor, Alison W. Bowers

и другие.

Open Book Publishers, Год журнала: 2025, Номер unknown, С. 143 - 154

Опубликована: Янв. 30, 2025

Nicole M. Ardoin, Ryan J. O’Connor, and Alison W. Bowers. In the social sciences, concept of “place” plays a large role in how humans relate to their environment. History culture, as well nuances human behavior, often revolve around person’s sense place belonging. Rather than assuming people are separate from nature, is common western science, this approach views fully engaged nature. By probing these connections between place, one can foster engagement individuals communities characterizing problems framing pathways solutions that promote sustainability marine socio-ecological systems. Deep scholarship well-developed case studies, presented here, support emerging thinking.

Язык: Английский

Процитировано

1
Effects of anthropogenic noise on marine mammal abundances informed by mixed methods DOI Creative Commons

Raymond J. O’Connor,

Nicole M. Ardoin, Giulio A. De Leo

и другие.

npj Ocean Sustainability, Год журнала: 2025, Номер 4(1)

Опубликована: Апрель 5, 2025

Язык: Английский

Процитировано

0
Engaging and legitimizing communities: co-designing a community-based Marine Protected Area DOI Creative Commons
Mafalda Rangel, Bárbara Horta e Costa, Maria Helena Guimarães

и другие.

Marine Policy, Год журнала: 2025, Номер 178, С. 106695 - 106695

Опубликована: Апрель 9, 2025

Язык: Английский

Процитировано

0
Exploring the diverse values local people associate with marine protected areas and the implications for sustainable ocean management DOI Creative Commons

Shun Kageyama,

Abigayil Blandon, Robert Blasiak

и другие.

Ocean & Coastal Management, Год журнала: 2024, Номер 261, С. 107523 - 107523

Опубликована: Дек. 20, 2024

Язык: Английский

Процитировано

1
Pescatourism’s contribution to the management effectiveness of the Marine Protected Area (MPA) of “Taza” (Algeria, Southwestern Mediterranean) DOI

Salah Eddine Guedri,

Amina Hana Djabi,

Ibrahim Yahiaoui

и другие.

Marine Policy, Год журнала: 2024, Номер 173, С. 106581 - 106581

Опубликована: Дек. 31, 2024

Язык: Английский

Процитировано

1
Community Scientists Fill Gaps in Knowledge Generation, Education, and Enforcement in Social-Ecological Systems DOI Creative Commons
Ryan O’Connor, Giulio A. De Leo, Nicole M. Ardoin

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 3, 2024

Abstract Community scientists play a critical role in developing, interpreting, and sharing the science needed to understand complex human ecological systems. In this paper, we present novel framework for analyzing interactions of community scientists, highlighting their vital filliing capacity gaps throughout social-ecological Our identifies three key boundaries spanned by scientists: knowledge generation, education, enforcement. We further show that these boundary spanning roles emerge response limitations institutions. provide valuable support institutions acting as “eyes ears,” augmenting supporting activities government agencies empirically through case study coastal California, USA. By emphasizing diverse ways scientists' engage with communities, our provides foundation more effective integration efforts environmental research policy. also highlight need careful consideration ethical practical implications relying on accommodate institutional capacity. This advances understanding offers broadly applicable insights improving collaborative approaches challenges across scales

Язык: Английский

Процитировано

0
A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm DOI Creative Commons

Yougui Ren,

Zhibin Zhao,

Junjian Jiang

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(18), С. 8535 - 8535

Опубликована: Сен. 22, 2024

With the rapid development of remote sensing image data, efficient retrieval target images interest has become an important issue in various applications including computer vision and sensing. This research addressed low-accuracy problem traditional content-based algorithms, which largely rely on comparing entire features without capturing sufficient semantic information. We proposed a scene graph similarity-based algorithm. Firstly, one-shot object detection algorithm was designed for based Siamese networks tailored to objects unknown class query image. Secondly, construction developed, their attributes spatial relationships. Several strategies were different relationships, full connections, random nearest star or ring connections. Thirdly, by making use edge feature extraction, extraction network established features. Fourthly, neural tensor network-based similarity calculation vectors obtain results. Fifthly, dataset named with graphs (RSSG) built testing, contained 929 corresponding generated developed strategies. Finally, through performance comparison experiments algorithms AMFMN, MiLaN, AHCL, precision rates, Precision@1 improved 10%, 7.2%, 5.2%, Precision@5 3%, 5%, 1.7%; Precision@10 1.7%, 0.6%. In recall Recall@1 2.5%, 4.3%, 1.3%; Recall@5 3.7%, 6.2%, 2.1%; Recall@10 4.4%, 7.7% 1.6%.

Язык: Английский

Процитировано

0