Human mobility modeling is critical for urban planning and transportation management, yet existing datasets often lack the resolution and semantic richness required for comprehensive analysis. To address this, we proposed a cross-domain data fusion framework that integrates multi-modal data of distinct nature and spatio-temporal resolution, including geographical, mobility, socio-demographic, and traffic information, to construct a privacy-preserving and semantically enriched human travel trajectory dataset. This framework is demonstrated through two case studies in Los Angeles (LA) and Egypt, where a domain adaptation algorithm ensures its transferability across diverse urban contexts. Quantitative evaluation shows that the generated synthetic dataset accurately reproduces mobility patterns observed in empirical data. Moreover, large-scale traffic simulations for LA County based on the generated synthetic demand align well with observed traffic. On California's I-405 corridor, the simulation yields a Mean Absolute Percentage Error of 5.85% for traffic volume and 4.36% for speed compared to Caltrans PeMS observations.

Abstract: Recent automated crop mapping via supervised le arning - based methods have demonstrated unprecedented improvement over classical techniques. However, m ost crop mapping studies are limited to same - year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Cross - y ear crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping ( VD TW), a novel multi - year classification approach based on warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral v ariations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with lim ited data. We carried out test s of our approach with Lan dsat 8 time - series imagery from years 2013 to 2016 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, we tested VDTW corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data . The VDTW method achieved 99.85% and 99.74% overall accuracies for the same and cross years, respectively with fewer training samples compared to oth er state - of - the - art approaches, i.e. spectral angle mapp er ( SAM), dynamic time warping ( DTW), time - weighted DTW ( TWDTW), random forest (RF), support vector machine ( SVM) and deep long short - term memory ( LSTM) methods. The proposed method could be expanded for other crop types and/or geographical areas. Keywords: Time series; phenology; multi - year classification; dynamic programming; Landsat; crop mapping; land use; corn; cotton; soybean 1. Introduction T he world population is expected to exceed nine billion in 2050 [1] . Providing adequate nutrition for the increasing human population is a significant concern. Advanced agri cultural technologies, such as precision agriculture and precision irrigation are rapidly emerging to optimize water, fertilizers, and pesticides; thereby enabling higher crop yield. Accurate crop maps are the first requirements of advanced agriculture app lications such as yield forecasting . Early - season crop yield estimates are a crucial factor for food security and monitor ing agricultural subventio ns. Crop maps are also an essential tool for statistical purposes to analyze annual changes in agricultural p roduction. However, there are a variety of field crops with similar phenologies and spectral signatures.