Rapid urbanization has brought many problems, including housing shortages, traffic congestion, air pollution, and lack of public space. To solve these problems, the United Nations proposed “The 2030 Agenda for Sustainable Development”, which contains 17 Sustainable Development Goals covering three dimensions: economy, society, and environment. Among them, Sustainable Development Goal 11 (SDG11), “Make cities and human settlements inclusive, safe, resilient and sustainable”, can be measured at the city level. So far SDG11 still lacks three-quarters of the data required to accurately assess progress towards the goal. In this paper, we localized the indicators of SDG11 and collected Earth observation data, statistical data, and monitoring data at the city and county levels to build a better urban sustainable development assessment framework. Overall, we found that Haikou and Sanya were close to achieving sustainable development goals, while other cities were still some distance away. In Hainan Province, there was a spatial distribution pattern of high development levels in the north and south, but low levels in the middle and west. Through the Moran’s I Index of Hainan Province, we found that the sustainable development of Hainan Province did not yet form part of integrated development planning. The sustainable development assessment framework and localization methods proposed in this paper at the city and county levels provide references for the sustainable development of Hainan. At the same time, it also provides a reference for the evaluation of county-level sustainable development goals in cities in China and even the world.

To address the highly demanding assessment of the quantity of fish in cages, a method for estimating the fish quantity in cages based on image sonar is proposed. In this method, forward-looking image sonar is employed for continuous detection in cages, and the YOLO target detection model with attention mechanism as well as a BP neural network are combined to achieve a real-time automatic estimation of fish quantity in cages. A quantitative experiment was conducted in the South China Sea to render a database for training the YOLO model and neural network. The experimental results show that the average detection accuracy mAP50 of the improved YOLOv8 is 3.81% higher than that of the original algorithm. The accuracy of the neural network in fitting the fish quantity reaches 84.63%, which is 0.72% better than cubic polynomial fitting. In conclusion, the accurate assessment of the fish quantity in cages contributes to the scientific and intelligent management of aquaculture and the rational formulation of feeding and fishing plans.

In order to accurately estimate the distribution of Trachinotus ovatus in marine cages, a novel method was developed using omnidirectional scanning sonar and deep-learning techniques. This method involved differentiating water layers and clustering data layer by layer to achieve precise location estimation. The approach comprised two main components: fish identification and fish clustering. Firstly, omnidirectional scanning sonar was employed to perform spiral detection within marine cages, capturing fish image data. These images were then labeled to construct a training dataset for an enhanced CS-YOLOv8s model. After training, the CS-YOLOv8s model was used to identify and locate fish within the images. Secondly, the cages were divided into water layers with depth intervals of 40 cm. The identification coordinate data for each water layer were clustered using the DBSCAN method to generate location coordinates for the fish in each layer. Finally, the coordinate data from all water layers were consolidated to determine the overall distribution of fish within the cage. This method was shown, through multiple experimental results, to effectively estimate the distribution of Trachinotus ovatus in marine cages, closely matching the distributions detected manually.

Sun, J.L.; Zhang, Z.Y.; Liu, X.; Zhang, C., Mou, Z.H., 2020. Analysis of ionospheric TEC anomalies during super typhoons Megi and Meranti. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 692-695. Coconut Creek (Florida), ISSN 0749-0208.In this paper, we detected and analyzed ionospheric anomalies during the 2016 super typhoons Megi and Meranti using the sliding interquartile range method applied to the ionospheric total electron content (TEC) grid data from the Global Ionospheric Model (GIM) provided by the Center for Orbit Determination in Europe (CODE). We found that ionospheric TEC anomalies occurred 2 days before the origination of super typhoon Megi. The anomalies occurred from 02:00 to 12:00 local time over a period of 10 h, with a maximum outlier of 12 TEC units over the typhoon center. Anomalies occurred in the ionosphere over both the area near the typhoon center and the corresponding equatorial magnetic conjugate region. The anomalies in both areas showed approximately the same trends. It is very likely that the disturbances from the ionospheric anomalies over the typhoon center were one of the precursors for the typhoon event. Similar ionospheric anomalies also occurred in the ionosphere 2 days before the formation and development of super typhoon Meranti. The global ionospheric TEC distribution was even more complex on that day. The existence of multiple peaks and valleys in the maximum outlier near the typhoon center was possibly associated with the position of the typhoon center and the GIM grid points or the wind speeds over the typhoon center.

Remote sensing object detection remains challenging because objects often appear with large scale variation, dense spatial layouts, and strong interference from complex geographical backgrounds. To address these coupled difficulties, we propose EDM-Net, an end-to-end multi-scale detector that organizes feature processing into three coordinated stages: adaptive extraction, intra-scale interaction, and cross-scale fusion. First, an efficient sparse mixture-of-experts (ES-MoE) module is embedded in the backbone to allocate scale-specific convolutional experts according to scene-level feature responses, providing a more adaptive feature basis than a single static extraction path. Second, a dynamic mixing intra-scale feature interaction (DMIFI) module is introduced into the Transformer encoder. This module combines global self-attention with dynamic spatial mixing, thereby preserving long-range context while reintroducing local two-dimensional inductive bias for dense and small objects. Third, a multi-scale synergistic attention fusion (MSAF) module aligns adjacent feature levels through parallel local and global attention branches and structural re-parameterization, reducing semantic dilution during feature aggregation. Comprehensive experiments on three large-scale remote sensing benchmark datasets, DIOR, NWPU VHR-10, and RSOD, demonstrate that EDM-Net consistently improves over the re-trained RT-DETR-R18 baseline under the same experimental protocol, attaining mAP50 scores of 83.7%, 95.6%, and 95.8% respectively. Additional ablation and scale-specific analyses indicate that the three modules contribute complementary gains, especially for small and densely distributed objects. These results suggest that coordinated extraction, interaction, and fusion can improve remote sensing object detection under complex scale and background conditions.

Identifying and localizing small objects in UAV imagery is a highly challenging task. Existing detection models are prone to missed detections, false detections, and inaccurate localization, while improvements in detection accuracy and real-time performance are often accompanied by an increase in the number of model parameters. To address these issues, this paper proposes an efficient end-to-end network for real-time small-object detection in UAV imagery, termed FBR-DETR. Through coordinated design across three stages, namely feature extraction, efficient encoding, and cross-scale fusion, the proposed network achieves higher detection accuracy and real-time performance for small objects with fewer parameters. FBR-DETR incorporates three core innovations. First, to address the tendency of small objects to be overwhelmed by background textures in deep features, we design a frequency-domain inverse-convolution-enhanced feature extraction network (FICE-Net), which introduces frequency-domain inverse convolution into the backbone feature extraction process to enhance the joint perception of global spectral structures and fine-grained spatial features at the source. Second, to reduce the inference overhead introduced by full-precision attention computation in AIFI, we construct a binary attention-based intra-scale feature interaction module (Binary-AIFI), which binarizes the query and key matrices while preserving global contextual representation with controlled additional computational cost. Third, to alleviate the dilution of small-object information during cross-scale fusion, we propose a re-parameterized cross-scale feature fusion module (RCFF), which enhances feature representation during training through a multi-branch re-parameterized structure and is equivalently merged into a single convolution during inference, thereby balancing fusion capability and real-time inference efficiency. On the VisDrone2019-DET and HIT-UAV datasets, the proposed method achieves mAP_0.5 values of 51.2% and 83.4%, respectively. Compared with the baseline model, our method improves mAP_0.5 by 5.3% and 5.9%, reduces the number of parameters by 30.8%, and improves the average precision for small objects (APs) by 2.4% and 4.9%, respectively.