The agricultural sector is one of the vital sectors in the world and has a major contribution to the achievement of the goals of the Sustainable Development Goals (SDGs) program. In the SDGs, attention to food security is focused on the second key indicator, namely zero hunger (SDG 2). The availability of accurate land cover data is needed as basic data for the raw area of rice fields that will be used to measure the level of food security. Plant mapping requires the processing and management of very large volumes of unstructured satellite image data which leads to Geo Big Data problems and demands new technology and resources capable of handling large amounts of satellite imagery. In particular, the emergence of cloud computing resources, such as Google Earth Engine, has addressed this Geo Big Data problem. We used the Random Forest (RF) algorithm on the Google Earth Engine (GEE) platform in North Jakarta City in 2019 to classify land cover. The results showed that the overall accuracy (OA)
Belgiu, M., & Drăguţ, L. (2016). Random Forest in Remote Sensing: A Review of Applications and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing 114, 24–31.
Breiman, L. (2001). Random forests. Machine learning, vol. 45, no. 1, 5–32.
Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC Press.
FAO. (2016). Food and Agriculture Key To Achieving The 2030 Agenda For Sustainable Development. FAO.
Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. (2016). Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE J. Sel. Topics Appl. Earth Observ, Remote Sens., vol. 9, no. 6, 2500-2508.
Mutanga, O., & Kumar, L. (2019). Google Earth Engine Applications. Remote Sens., 11, 591.
Omilola, B., & Robele, S. (2017). The Central Position of Agriculture within the 2030 Agenda for Sustainable Development. Washington, DC: International Food Policy Research Institute.
Parsa, I. M., Dirgahayu, D., Manalu, J., & Carolita, I. (2017). Uji Model Fase Pertumbuhan Padi Berbasis Citra Modis Multiwaktu Di Pulau Lombok. Jurnal Penginderaan Jauh, 51-64.
Ramankutty, N., Mehrabi, Z., Waha, K., Jarvis, L., Kremen, C., Herrero, M., le ba bangwe. (2018). Trends in global agricultural land use: implications for environmental health and food security. Annu. Rev. Plant Biol. 69, 789–815.
Sammut, C., & Webb, G. I. (2017). Encyclopedia of Machine Learning and Data Mining. New York, AS: Springer.
Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring google earth engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Front. Earth Sci., vol. 5, 17.
Teluguntla, P., & Thenkabail, P. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS J. Photogramm. Remote Sens., 325-340.
Thenkabail, P., Lyon, J., & Huete, A. (2019). Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation. Boca Raton: CRC Press.
Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The Kappa Statistic. Family Medicine, 37, 360-63.
Visa, S., Ramsay, B., Ralescu, A., & Van Der Knaap, E. (2011). Confusion matrix-based feature selection. MAICS, 710, 120–127.
Xue, J., & Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 1–17.
You, J., Li, X., Low, M., Lobell, D., & Ermon, S. (2017). Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. 31th AAAI Conf. Artificial Intelligence, (dits. 4559–4565).
Zhang, C., Zhang, H., & Zhang, L. (2018). An Automated Paddy Rice Extent Extraction with Time Stacks of Sentinel Data: A Case Study In Jianghan Plain, Hubei, China. 7th Int. Conference Agro-Geoinformatics 8, (dits. 1-6).