As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth’s surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification.

Land Use and Land Cover Classification Meets Deep Learning: A Review

Water quality is an important factor affecting marine pasture farming. Water quality parameters have the characteristics of time series, showing instability and nonlinearity. Previous water quality prediction models are usually based on specific assumptions and model parameters, which may have limitations for complex water environment systems. Therefore, in order to solve the above problems, this paper combines long short-term memory (LSTM) and backpropagation (BP) neural networks to construct an LSTM-BP combined water quality parameter prediction model and uses the root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) to evaluate the model. Experimental results show that the prediction performance of the LSTM-BP model is better than other models. On the RMSE and MAE indicators, the LSTM-BP model is 76.69% and 79.49% lower than other models, respectively. On the NSE index, the LSTM-BP model has improved by 34.13% compared with other models. The LSTM-BP model can effectively reflect time series characteristics and nonlinear mapping capabilities. This research provides a new method and reference for the prediction of water quality parameters in marine ranching and further enables the intelligent and sustainable development of marine ranching. 

Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP

Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks

There is ongoing debate regarding the merits of decriminalization or outright legalization of commercial sex work in the United States. A few municipalities have officially legalized both the selling and purchasing of sex, while others unofficially criminalize purchasing sex but have decriminalized its sale. In addition, there are many other locales with no official guidance on the subject but have unofficially decriminalized sex work by designating specific areas in an urban landscape safe from law enforcement for commercial sex, by quietly ceasing to arrest sex sellers, or by declining to prosecute anyone selling or attempting to sell sex. Despite these efforts, it remains crucial to understand where in an urban area commercial sex exchanges occur—legalization and decriminalization may result in fewer arrests but is likely to increase the overall size of the sex market. This growth could result in an increase in sex trafficking victimization, which makes up the majority of commercial sex sellers in any domestic market. Given the distribution of prostitution activities in most communities, it is possible to use high-fidelity predictive models to identify intervention opportunities related to sex trafficking victimization. In this research, we construct several machine learning models and inform them with a range of known criminogenic factors to predict locations hosting high levels of prostitution. We demonstrate these methods in the city of Chicago, Illinois. The results of this exploratory analysis identified a range of explanatory factors driving prostitution activity throughout Chicago, and the best-performing model correctly predicted prostitution frequency with 94% accuracy. We conclude by exploring specific areas of under- and over-prediction throughout Chicago and discuss the implications of these results for allocating social support efforts.

/pdf/prostitution-arrest-spatial-forecasting-in-an-era-of-26oijfsn.pdf

Prostitution Arrest Spatial Forecasting in an Era of Increasing Decriminalization

基于权重动态变形和双重网络自我验证的遥感影像分类方法

ABSTRACT Recent developments in deep learning (DL) techniques have provided a series of new methods for land cover classification. However, most DL-based methods do not consider the rich spatial association of land cover classes embedded in remote sensing images. In this research, a deep relearning method based on the recurrent neural network (DRRNN) is proposed for land cover classification. The relearning approach has great potential to improve classification, which has never been used in DL-based land cover classification. To utilize the spatial association of the pixels’ information classes, a class correlated feature (CCF) is first extracted in a local window from an initial classification result. This feature can reflect both the spatial autocorrelation and spatial arrangement of land cover classes. Since the recurrent neural network (RNN) is designed to process sequential data, the CCF is formed as a feature sequence, allowing RNN to model the dependency between class labels. The relearning process is then applied to iteratively classify remote sensing images based on the CCF and spectral-spatial feature. At each relearning iteration, the CCF is learned from the previous classification result until a stopping condition is satisfied. This method was tested on five remote sensing images with different sensors and diverse environments. It was observed that noise in the classification result can be filtered by considering spatial autocorrelation, and misclassified areas can be corrected by incorporating spatial arrangement in the relearning process. The classification results indicate that compared to other state-of-the-art DL methods, the proposed method consistently achieves the highest accuracy.

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2115589?needAccess=true

A deep relearning method based on the recurrent neural network for land cover classification

In recent years, with the continuous development of the Internet, online shopping has gradually become a mainstream shopping method for people. At the same time, people often comment on the purchased goods, which expresses their shopping feelings on the one hand and provides corresponding reference for others' shopping on the other. In order to better mine and analyze the massive review data and extract valuable reviews from them, this paper proposes an improved random forest classification method based on Word2vec, which optimizes the number of decision trees and the number of leaf node variables in the random forest algorithm by incorporating the seagull algorithm to classify the crawled e-mall review data emotionally. The results show that the word2vec+ISOA-RF classification model is improved more significantly and can effectively enhance the performance of the classification algorithm.

An improved random forest order review classification method based on Word2vec

Aiming at the problem of vehicle detection in UAV remote sensing images, adaptive anchor frame calculation is used to seek the optimal initial value anchor frame, the loss function of YOLOv5 algorithm which only uses the length width ratio of the bounding box and cannot reflect the real difference between the width and height of the bounding box and its real value, is improved. It eliminates the problem of slow convergence caused by the failure to reflect the real difference in width and height. Using this improved YOLOv5 algorithm for training and detection, the effectiveness of the algorithm for vehicle detection in UAV aerial images is verified.

Vehicle Detection in UAV Remote Sensing Image Based on Improved YOLOv5

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A deep relearning method based on the recurrent neural network for land cover classification

An improved random forest order review classification method based on Word2vec

Vehicle Detection in UAV Remote Sensing Image Based on Improved YOLOv5