Land | Feature Papers in the Section “Land Innovations: Data and Machine Learning” from the First Half of 2024

We are delighted to present a list of papers that were published in the Section “Land Innovations: Data and Machine Learning” in the first half of 2024 in Land (ISSN: 2073-445X) and have received extensive attention.

1. “A Proposed Methodology for Determining the Economically Optimal Number of Sample Points for Carbon Stock Estimation in the Canadian Prairies”
by Preston Thomas Sorenson, Jeremy Kiss and Angela Bedard-Haughn
Land 2024, 13(1), 114; https://doi.org/10.3390/land13010114
Available online: https://www.mdpi.com/2073-445X/13/1/114

2. “A Land Administration Data Exchange and Interoperability Framework for Kenya and Its Significance to the Sustainable Development Goals”
by Clifford Okembo, Javier Morales, Christiaan Lemme, Jaap Zevenbergen and David Kuria
Land 2024, 13(4), 435; https://doi.org/10.3390/land13040435
Available online: https://www.mdpi.com/2073-445X/13/4/435

3. “Bridging Sustainable Development Goals and Land Administration: The Role of the ISO 19152 Land Administration Domain Model in SDG Indicator Formalization”
by Mengying Chen, Peter Van Oosterom, Eftychia Kalogianni, Paula Dijkstra and Christiaan Lemmen
Land 2024, 13(4), 491; https://doi.org/10.3390/land13040491
Available online: https://www.mdpi.com/2073-445X/13/4/491

4. “Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid”
by Gladys Elizabeth Kenyon, Dani Arribas-Bel and Caitlin Robinson
Land 2024, 13(5), 575; https://doi.org/10.3390/land13050575
Available online: https://www.mdpi.com/2073-445X/13/5/575

5. “Towards a Comprehensive Framework for Regional Transportation Land Demand Forecasting: Empirical Study from Yangtze River Economic Belt, China”
by Ke Wang, Li Wang and Jianjun Zhang
Land 2024, 13(6), 847; https://doi.org/10.3390/land13060847
Available online: https://www.mdpi.com/2073-445X/13/6/847

You can find more information about Land and the “Land Innovations: Data and Machine Learning” Section at the following link: https://www.mdpi.com/journal/land/sections/land_innovations_data_machine_learning.

Land Editorial Office