A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The Gini index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K.

Rapid land-use change detection (LUCD) is pivotal for refined urban planning and management. In this paper, we investigate LUCD through learning embeddings of points of interest (POIs) from multiple temporalities. There are several prominent challenges: (1) the co-occurrence problem of multi-temporal POIs, (2) the heterogeneity of POI categorization, and (3) The lack of human-crafted labels. Therefore, multi-temporal POIs need to be aligned in the embedding space for effective LUCD. This study proposes a multi-temporal POI embedding (MT-POI2Vec) technique for LUCD in a fully unsupervised manner. In MT-POI2Vec, we first utilize random walks in POI networks to capture their single-period co-occurrence patterns; then, we leverage manifold learning to capture (1) single-period categorical semantics of POIs to enforce semantically similar POI embedding to be close and (2) cross-period categorical semantics to align multi-temporal POI embedding in a unified embedding space. We conducted experiments in Shenzhen, China, which demonstrates that the proposed method is effective. Compared with several baseline models, MT-POI2Vec can better align multi-temporal POIs and thus achieve higher performance in LUCD. In addition, our model can effectively identify areas with unchanged land use and land use changes in residential and industrial areas at a fine scale.

The formulation of mixed urban land uses is not only intended to find the ideal scenario of land use but also regarded as a way toward sustainable urban development. We propose a geo-semantic mining approach Traj2Vec to quantify the trajectories of residents as high-dimensional semantic vectors. Then, a random forest (RF) method is used to model the relationship between the semantic vectors and mixed urban land uses. The proposed Traj2Vec approach can obtain the highest accuracy (OA = 0.7733, kappa = 0.7245) in urban land-use classification and a high average proportion accuracy (64.0%) in capturing the proportions of urban land-use types. Diversity analysis indicates that Shenzhen has a high degree of mixed urban land use at the scale of a street block. By analyzing the mixing index and the travel distance, we find a weak but significant negative correlation between them (r=−0.107, p<0.001), which not only confirms the conclusion that an increase in the degree of mixing will reduce the travel distances of residents but also verifies the mixing index. This suggests that urban planning should focus on mixed urban land uses, which can reduce the travel distances of residents, reduce energy consumption, and make cities more compact.

Summary The performance of active contour model is limited on retinal vessel segmentation as vessel images are usually corrupted with intensity inhomogeneity, low contrast, and weak boundary, which severely affect the segmentation results of retinal vessels. A new active contour model combining the local and global information is proposed in this paper to facilitate the vessel segmentation. In our model, the fuzzy conception is firstly introduced as fuzzy methods generally provide more accurate and robust clustering and the concept of fuzziness in fuzzy clustering, which is represented by membership, can reflect the intensity distribution of the image. Then, we define local energy based on Maximum a Posterior Probability and use spatially varying parameters, mean and stand deviation, to describe the local Gaussian distribution in order to better deal with intensity inhomogeneity. Furthermore, we combine local and global energy based on fuzzy clustering, with a weight coefficient. The coefficient is computed by a weight function according to contrast ratio of the image. Experiments on synthetic and real images and comparisons with other state‐of‐the‐art active contour models show that the proposed model can detect objects more accurate and robust, especially for vessels on retinal angiogram.

Diabetes, hypertension, cerebral arteriosclerosis and other diseases have become great threats to human health, so it is urgent to explore their initial symptoms for early prevention and treatment. As an important part of small and medium-sized vessels of human body, retinal vessel is the only deep capillary that can be non-traumatic directly observed and its morphology, such as vascular diameter, shape and distribution, is deeply influenced by these diseases. So an effective vascular detection and features measurement will help make more accurate diagnosis of these diseases. This paper proposes a local adaptive segmentation to detect more accurate retinal vascular network from abnormal retinal images which contain red and bright lesions. The retinal image is firstly segmented by weighted entropy with probability segmentation to detect preliminary vascular network. Then a two-dimensional partial differential matched filter is introduced into segmentation to differentiate lesions from vascular network based on a vascular property. The algorithm has been tested and compared with other vascular network segmentation algorithms on the publicly available STARE database since it contains retinal images where the vascular structure has been precisely marked by two experts. The experiments demonstrate that our approach is capable of detecting the vascular network effectively, offering a better segmentation results, especially on abnormal cases. Because of its effectiveness, simplicity and robustness for different image conditions, it is suitable for automated vascular analysis.