Lechan Yang^1,*, Li Li², Shouming Ma³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1471-1489, 2024, DOI:10.32604/cmc.2024.056379 - 15 October 2024

Abstract Accurate geospatial data are essential for geographic information systems (GIS), environmental monitoring, and urban planning. The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data. In this paper, we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery. Existing geospatial data recovery methods require complete datasets for training, resulting in time-consuming data recovery and lack of generalization. To address these issues, we propose a GAIN-LSTM-based geospatial data recovery method (TGAIN), which consists of two main works:… More >

Maan Habib^1,*, Maan Okayli²

Revue Internationale de Géomatique, Vol.32, pp. 1-16, 2023, DOI:10.32604/rig.2023.043399 - 30 September 2023

Abstract This study provides a comprehensive overview of modern cartography innovations and emerging trends, highlighting the importance of geospatial representation in various fields. It discusses recent advancements in geospatial data collection techniques, including satellite and aerial imagery, Light Detection and Ranging (LiDAR) technology, and crowdsourcing. The research also investigates the integration of big data, machine learning, and real-time processing in Geographic Information Systems (GIS), as well as advances in geospatial visualization. In addition, it examines the role of cartography in addressing global challenges such as climate change, disaster management, and urban planning in line with the More >

Amena Mahmoud^1,*, Mahmoud Y. Shams², O. M. Elzeki³, Nancy Awadallah Awad⁴

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2711-2726, 2021, DOI:10.32604/cmc.2021.015293 - 13 April 2021

Abstract Semantic Web (SW) provides new opportunities for the study and application of big data, massive ranges of data sets in varied formats from multiple sources. Related studies focus on potential SW technologies for resolving big data problems, such as structurally and semantically heterogeneous data that result from the variety of data formats (structured, semi-structured, numeric, unstructured text data, email, video, audio, stock ticker). SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data. In the current research, we implement a new… More >