Identification of land use for crops in Jawa Timur province based on multispectral imaging by combining multi-layer density-based spatial clustering of applications with noise and time-weighted dynamic time wrapping

Ahmad Rifan Ferdiyansyah, Dedy Dwi Prastyo*, Kartika Fithriasari

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Up-to-date information about land use from crops can improve the quality of agricultural statistical data in Indonesia. However, updating activities through ground checks generally requires considerable resources so that various alternative methods continue to be developed to identify land use for agriculture. One alternative to identify land use is to utilize remote sensing data using the time-weighted Dynamic Time Wrapping (TWDTW) method. Several studies have proven that the TWDTW method can produce fairly accurate predictions. However, their studies are limited to small areas or only focused on a single food crop. When applied to a larger area with highly varied cropping patterns, the TWDTW method tends to produce inaccurate predictions. Therefore, this study combines methods to predict land use in large areas with various cropping patterns. This combination method involves creating subclasses using Multi-Layer DBSCAN to capture variations in cropping patterns, followed by predicting land use using the TWDTW method. In general, prediction results using the TWDTW method have better accuracy than those using DTW or Euclidean. In addition, combining them with the Multi-Layer DBSCAN method will increase the accuracy of the predictions.

Original languageEnglish
Article number060007
JournalAIP Conference Proceedings
Volume3201
Issue number1
DOIs
Publication statusPublished - 15 Nov 2024
Event9th SEAMS-UGM International Conference on Mathematics and its Applications 2023: Integrating Mathematics with Artificial Intelligence to Broaden its Applicability through Industrial Collaborations - Yogyakarta, Indonesia
Duration: 25 Jul 202328 Jul 2023

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