Monitoring the quality of the urban ecological environment has become one of the important elements of promoting a sustainable urban development. The remote sensing ecological index (RSEI) provides a new research direction in urban ecological environment monitoring, combined with remote sensing. However, by using the principal component analysis method in RSEI, the calculation results are complicated and the workload is huge. To effectively assess the urban ecological environment, an improved remote sensing ecological index (IRSEI) was created to improve the ease of data use by using the entropy weighting method with spatiotemporal characteristics and seasonal variations. Furthermore, a geographically weighted regression model was used to quantify the impact of human activities on the urban ecological environment quality. The results showed that the IRSEI could provide a new method for monitoring the urban ecological environment quality, which makes the work easier while ensuring the validity of the results. It was concluded that (1) seasonal differences in the ecological quality of the study area were evident in the IRSEI model and the overall ecological environment quality of Jining City had been on an upward trend in the past 20 years; (2) the ecological quality in the study area was unevenly distributed spatially, with the southwestern part being better than the northeastern part, and the ecological grade being predominantly between moderate and good; and (3) the spatial aggregation effect of the IRSEI was increasing with time. The geographically weighted regression (GWR) revealed the influence of human activities on the ecological environment quality, among which economic level was positively related to ecological improvement, but the population density and night light index were negatively related to improvements in the ecological environment; road network density only showed a negative correlation in 2020. As Jining urbanizes, attention should be paid to protecting the built environment and population distribution.

Crop type mapping visualizes the spatial distribution pattern and proportion of planting areas of different crop types, which is the basis for subsequent agricultural applications. Although optical remote sensing has been widely used to monitor crop dynamics, data are not always available due to cloud and other atmospheric effects on optical sensors. Satellite microwave systems such as Synthetic Aperture Radar (SAR) have all-time and all-weather advantages in monitoring ground and crop conditions, combining optical imagery and SAR imagery for crop type classification is of great significance. Our study mainly proposes seven feature combination schemes based on the combination of multi-temporal spectral features and texture features of Sentinel-2 (S2), and radar backscattering features of Sentinel-1 (S1) evaluate the influence of different data sources and different features on classification accuracy, obtains the optimal classification strategy and analyses the contribution of different features to classification result, in the aim of providing a new technical approach for the fine identification of crops from multi-source remote-sensing data. Results show that the crop classification accuracy of combined multi-time series spectral, texture, and radar features is higher than that of combining two types of features. The features subset selected by multi-period spectral, texture, and radar features have the best classification result, the overall accuracy (OA) and kappa coefficients reach 96.40% and 0.93, respectively. The study provides a method reference for future research on larger-scale remote-sensing crop precise extraction.

Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments.

The study of urban functional area identification is of great significance for urban function cognition, spatial planning, and economic development. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Aiming at the above problems, this paper proposes a multi-scale recursive recognition method based on interactive validation for urban functional areas using taxi trajectory data and point of interest (POI) data as the main data sources. First, the dynamic time warping (DTW) algorithm generates a time series similarity matrix, a CA-RFM model combining the clustering algorithm and random forest model is constructed. The model extracts significant feature regions as inputs through a K-medoid clustering algorithm, which are imported into the random forest model for urban functional zone (UFZ) identification. Then, to overcome the shortcomings of a single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area, the results show that the combination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the study demonstrates the superiority of the proposed method in two aspects, namely, faster delineation and improved accuracy in urban functional area identification.

In recent years, the emergence of spatiotemporal big data has made the transition of functional identification from the physical dimension to socioeconomic or human activities becoming more common. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Using taxi trajectory data and point of interest (POI) data as the main data source, this study proposes a multi-scale recursive identification method for urban functional areas based on cross-validation. First, used the dynamic time warping (DTW) algorithm generates a time series similarity matrix, the CA-RFM model combines the clustering algorithm and random forest model is constructed, the model uses a clustering algorithm (K-MEDOIDS) to extract sig-nificant feature regions as input, which are imported into the random forest model for UFZ identification. Then, to overcome the shortcomings of single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method, obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area for the case study, the results show that the com-bination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the research shows the superiority of the multi-scale recursive identification method that fuses multi-source data and performs cross-validation from two aspects, that is, the division speed of urban functional area identification results is accelerated and the accuracy is improved.