Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Aninda Wisaksanti Rudiastuti, Nur Mohammad Farda, Dadan Ramdani

Judul : Mapping built-up land and settlements: a comparison of machine learning algorithms in Google Earth engine

Abstrak :
Monitoring the growth of built-up land finds its challenge as a never-ending mapping process. Since the built-up maps are taken into account in development planning and measuring the achievement of SDGs objectives (Goal 11 - indicator 11.3.1), the best mapping method should be attempted. Therefore, all efforts were worked on to speed up the mapping process without compromising accuracy. Various methods have been proposed, but numerous difficulties remain in accurate and efficient built-up and settlement extraction. With the combination of passive and active sensors images, we try several machine learning methods using Google Earth Engine (GEE) platforms to map built-up land and settlements in Purwokerto, Banyumas, Central Java. Around 369 samples were occupied to distinguish four classes of land covers: settlements, built-up land, waters, and others. The decision tree-based algorithms give the best performances, scilicet Random Forest (RF) and Gradient Tree Boost (GTB). Random forest is a collection of many decision trees, while Gradient Boosting is a machine learning algorithm that uses an ensemble of decision trees to predict values. Thus, the algorithms can handle complex patterns and data when linear models cannot. On the whole, RF and GTB classifiers can distinguish between settlements and non-settlement with an overall accuracy of 80%. The Support Vector Machine (SVM) classifier produces 71.43% accuracy with Kappa = 0.61, and the Minimum Distance (Mahalanobis) classifier gain overall accuracy of 74.29% (Kappa = 0.64).

Tahun : 2021 Media Publikasi : Prosiding

Kategori : Prosiding No/Vol/Tahun : 6 / 12082 / 2021

ISSN/ISBN : 12.2619493

PTN/S : Universitas Pakuan Program Studi : TEKNIK GEODESI

Bibliography :
[1] P. Prokop, “Remote sensing of severely degraded land: Detection of long-term land-use changes using high-resolution satellite images on the Meghalaya Plateau, northeast India,” Remote Sens. Appl. Soc. Environ., vol. 20, no. July, 2020.

[2] M. Pesaresi, C. Corbane, A. Julea, A. J. Florczyk, V. Syrris, and P. Soille, “Assessment of the added-value of sentinel-2 for detecting built-up areas,” Remote Sens., vol. 8, no. 4, 2016.

[3] N. M. Farda, “Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine,” IOP Conf. Ser. Earth Environ. Sci., vol. 98, no. 1, 2017.

[4] Z. Assarkhaniki, S. Sabri, and A. Rajabifard, “Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement,” Big Earth Data, vol. 00, no. 00, pp. 1–30, 2021.

[5] A. Boguszewski, D. Batorski, N. Ziemba-Jankowska, T. Dziedzic, and A. Zambrzycka, “LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery,” pp. 1102–1110, 2021.

[6] R. Septiani, I. P. A. Citra, and A. S. A. Nugraha, “Perbandingan Metode Supervised Classification dan Unsupervised Classification terhadap Penutup Lahan di Kabupaten Buleleng,” J. Geogr. Media Inf. Pengemb. dan Profesi Kegeografian, vol. 16, no. 2, pp. 90–96, 2019.

[7] L. M. Putri and P. Wicaksono, “MAPPING OF LAND USE CHANGES IN THE CORE ZONE OF PARANGTRITIS SAND DUNES USING OBIA METHOD 2015-2020,” J. Geogr., vol. 13, no. 1, pp. 109–120, 2021.

[8] P. A. Tavares, N. E. S. Beltrão, U. S. Guimarães, and A. C. Teodoro, “Integration of sentinel-1 and sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon,” Sensors (Switzerland), vol. 19, no. 5, 2019.

[9] A. Varshney, “Improved NDBI differencing algorithm for built-up regions change detection from remote-sensing data: An automated approach,” Remote Sens. Lett., vol. 4, no. 5, pp. 504–512, 2013.

[10] I. N. Hidayati, R. Suharyadi, and P. Danoedoro, “Kombinasi Indeks Citra untuk Analisis Lahan Terbangun dan Vegetasi Perkotaan,” Maj. Geogr. Indones., vol. 32, no. 1, p. 24, 2018.

[11] I. N. Hidayati, R. Suharyadi, and P. Danoedoro, “Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index,” Forum Geogr., vol. 32, no. 1, pp. 96–108, 2018.

[12] H. Ji, X. Li, X. Wei, W. Liu, L. Zhang, and L. Wang, “Mapping 10-m resolution rural settlements using multi-source remote sensing datasets with the google earth engine platform,” Remote Sens., vol. 12, no. 17, pp. 1–23, 2020.

[13] P. Gong, X. Li, and W. Zhang, “40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing,” Sci. Bull., vol. 64, no. 11, pp. 756–763, 2019.

[14] M. K. Bennett, N. Younes, and K. Joyce, “Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine,” Drones, vol. 4, no. 3, p. 50, 2020.

[15] C. Qiu, M. Schmitt, H. Taubenbock, and X. X. Zhu, “Mapping human settlements with multi-seasonal sentinel-2 imagery and attention-based resnext,” 2019 Jt. Urban Remote Sens. Event, JURSE 2019, pp. 0–3, 2019.

[16] P. E. Osgouei, S. Kaya, E. Sertel, and U. Alganci, “Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery,” Remote Sens., vol. 11, no. 3, pp. 1–24, 2019.

[17] J. Holloway, K. Mengersen, and K. Helmstedt, “Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals,” ARC Cent. Excell. Math. Stat. Front. (ACEMS); Sch. Math. Sci. Sci. Eng. Fac., no. September, pp. 19–21, 2018.

[18] Q. Zhang, C. Schaaf, and K. C. Seto, “The Vegetation adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity,” Remote Sens. Environ., vol. 129, pp. 32–41, 2013.

[19] G. A. Chulafak, D. Kushardono, and N. Zylshal, “Optimasi Parameter Dalam Klasifikasi Spasial Penutup Penggunaan Lahan Menggunakan Data Sentinel Sar (Parameters Optimization in Spatial Land Use Land Cover Classification Using Sentinel Sar Data),” J. Penginderaan Jauh dan Pengolah. Data Citra Digit., vol. 14, no. 2, pp. 111–130, 2018.

[20] M. N. Fathoni, G. A. Chulafak, and D. Kushardono, “Kajian Awal Pemanfaatan Data Radar Sentinel-1 untuk Pemetaan Lahan Baku Sawah di Kabupaten Indramayu Jawa Barat,” Semin. Nas. Penginderaan jauh ke-4, no. October, pp. 179–186, 2017.

[21] J. C. Thouret, Z. Kassouk, A. Gupta, S. C. Liew, and A. Solikhin, “Tracing the evolution of 2010 Merapi volcanic deposits (Indonesia) based on object-oriented classification and analysis of multi-temporal, very high resolution images,” Remote Sens. Environ., vol. 170, pp. 350–371, 2015.

[22] I. K. Noviyanti et al., “ANALISIS KETERSEDIAAN RUANG TERBUKA HIJAU DENGAN NDVI MENGGUNAKAN CITRA SATELIT WORLDVIEW-2 DI KOTA ( Analysis of The Availability of Green Open Space with NDVI using Worldview-2 in Yogyakarta City ),” no. 26, pp. 63–70, 2019.

[23] A. Elmes et al., “Accounting for training data error in machine learning applied to earth observations,” Remote Sens., vol. 12, no. 6, pp. 1–39, 2020.

[24] H. Wulansari, “Uji Akurasi Klasifikasi Penggunaan Lahan dengan Menggunakan Metode Defuzzifikasi Maximum Likelihood Berbasis Citra Alos Avnir-2,” BHUMI J. Agrar. dan Pertanah., vol. 3, no. 1, p. 98, 2017.

[25] M. S. Rini, “Kajian kemampuan metode neural network untuk klasifikasi penutup lahan dengan menggunakan Citra Landsat-8 OLI (kasus di Kota Yogyakarta dan sekitarnya),” Geomedia Maj. Ilm. dan Inf. Kegeografian, vol. 16, no. 1, pp. 1–12, 2018.

[26] J. Holloway and K. Mengersen, “Statistical machine learning methods and remote sensing for sustainable development goals: A review,” Remote Sens., vol. 10, no. 9, 2018.

[27] M. Schmidt, M. Pringle, R. Devadas, R. Denham, and D. Tindall, “A framework for large-area mapping of past and present cropping activity using seasonal landsat images and time series metrics,” Remote Sens., vol. 8, no. 4, 2016.

[28] R. A. A. L. M. Agris and R. A. B. Arreto, “Mapping and assessment of protection of mangrove habitats in Brazil,” Panam. J. Aquat. Sci., vol. 5, no. Ong 1995, pp. 546–556, 2010.

[29] A. Mellor, “The impact of training data characteristics on ensemble classification of land cover A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy,” RMIT University, 2017.

URL : https://doi.org/10.1117/12.2619493

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Detail Karya Ilmiah Dosen

Aninda Wisaksanti Rudiastuti, Nur Mohammad Farda, Dadan Ramdani

Judul	:	Mapping built-up land and settlements: a comparison of machine learning algorithms in Google Earth engine
Abstrak	:	Monitoring the growth of built-up land finds its challenge as a never-ending mapping process. Since the built-up maps are taken into account in development planning and measuring the achievement of SDGs objectives (Goal 11 - indicator 11.3.1), the best mapping method should be attempted. Therefore, all efforts were worked on to speed up the mapping process without compromising accuracy. Various methods have been proposed, but numerous difficulties remain in accurate and efficient built-up and settlement extraction. With the combination of passive and active sensors images, we try several machine learning methods using Google Earth Engine (GEE) platforms to map built-up land and settlements in Purwokerto, Banyumas, Central Java. Around 369 samples were occupied to distinguish four classes of land covers: settlements, built-up land, waters, and others. The decision tree-based algorithms give the best performances, scilicet Random Forest (RF) and Gradient Tree Boost (GTB). Random forest is a collection of many decision trees, while Gradient Boosting is a machine learning algorithm that uses an ensemble of decision trees to predict values. Thus, the algorithms can handle complex patterns and data when linear models cannot. On the whole, RF and GTB classifiers can distinguish between settlements and non-settlement with an overall accuracy of 80%. The Support Vector Machine (SVM) classifier produces 71.43% accuracy with Kappa = 0.61, and the Minimum Distance (Mahalanobis) classifier gain overall accuracy of 74.29% (Kappa = 0.64).
Tahun	:	2021	Media Publikasi	:	Prosiding
Kategori	:	Prosiding	No/Vol/Tahun	:	6 / 12082 / 2021
ISSN/ISBN	:	12.2619493
PTN/S	:	Universitas Pakuan	Program Studi	:	TEKNIK GEODESI
Bibliography	:	[1] P. Prokop, “Remote sensing of severely degraded land: Detection of long-term land-use changes using high-resolution satellite images on the Meghalaya Plateau, northeast India,” Remote Sens. Appl. Soc. Environ., vol. 20, no. July, 2020. [2] M. Pesaresi, C. Corbane, A. Julea, A. J. Florczyk, V. Syrris, and P. Soille, “Assessment of the added-value of sentinel-2 for detecting built-up areas,” Remote Sens., vol. 8, no. 4, 2016. [3] N. M. Farda, “Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine,” IOP Conf. Ser. Earth Environ. Sci., vol. 98, no. 1, 2017. [4] Z. Assarkhaniki, S. Sabri, and A. Rajabifard, “Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement,” Big Earth Data, vol. 00, no. 00, pp. 1–30, 2021. [5] A. Boguszewski, D. Batorski, N. Ziemba-Jankowska, T. Dziedzic, and A. Zambrzycka, “LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery,” pp. 1102–1110, 2021. [6] R. Septiani, I. P. A. Citra, and A. S. A. Nugraha, “Perbandingan Metode Supervised Classification dan Unsupervised Classification terhadap Penutup Lahan di Kabupaten Buleleng,” J. Geogr. Media Inf. Pengemb. dan Profesi Kegeografian, vol. 16, no. 2, pp. 90–96, 2019. [7] L. M. Putri and P. Wicaksono, “MAPPING OF LAND USE CHANGES IN THE CORE ZONE OF PARANGTRITIS SAND DUNES USING OBIA METHOD 2015-2020,” J. Geogr., vol. 13, no. 1, pp. 109–120, 2021. [8] P. A. Tavares, N. E. S. Beltrão, U. S. Guimarães, and A. C. Teodoro, “Integration of sentinel-1 and sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon,” Sensors (Switzerland), vol. 19, no. 5, 2019. [9] A. Varshney, “Improved NDBI differencing algorithm for built-up regions change detection from remote-sensing data: An automated approach,” Remote Sens. Lett., vol. 4, no. 5, pp. 504–512, 2013. [10] I. N. Hidayati, R. Suharyadi, and P. Danoedoro, “Kombinasi Indeks Citra untuk Analisis Lahan Terbangun dan Vegetasi Perkotaan,” Maj. Geogr. Indones., vol. 32, no. 1, p. 24, 2018. [11] I. N. Hidayati, R. Suharyadi, and P. Danoedoro, “Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index,” Forum Geogr., vol. 32, no. 1, pp. 96–108, 2018. [12] H. Ji, X. Li, X. Wei, W. Liu, L. Zhang, and L. Wang, “Mapping 10-m resolution rural settlements using multi-source remote sensing datasets with the google earth engine platform,” Remote Sens., vol. 12, no. 17, pp. 1–23, 2020. [13] P. Gong, X. Li, and W. Zhang, “40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing,” Sci. Bull., vol. 64, no. 11, pp. 756–763, 2019. [14] M. K. Bennett, N. Younes, and K. Joyce, “Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine,” Drones, vol. 4, no. 3, p. 50, 2020. [15] C. Qiu, M. Schmitt, H. Taubenbock, and X. X. Zhu, “Mapping human settlements with multi-seasonal sentinel-2 imagery and attention-based resnext,” 2019 Jt. Urban Remote Sens. Event, JURSE 2019, pp. 0–3, 2019. [16] P. E. Osgouei, S. Kaya, E. Sertel, and U. Alganci, “Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery,” Remote Sens., vol. 11, no. 3, pp. 1–24, 2019. [17] J. Holloway, K. Mengersen, and K. Helmstedt, “Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals,” ARC Cent. Excell. Math. Stat. Front. (ACEMS); Sch. Math. Sci. Sci. Eng. Fac., no. September, pp. 19–21, 2018. [18] Q. Zhang, C. Schaaf, and K. C. Seto, “The Vegetation adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity,” Remote Sens. Environ., vol. 129, pp. 32–41, 2013. [19] G. A. Chulafak, D. Kushardono, and N. Zylshal, “Optimasi Parameter Dalam Klasifikasi Spasial Penutup Penggunaan Lahan Menggunakan Data Sentinel Sar (Parameters Optimization in Spatial Land Use Land Cover Classification Using Sentinel Sar Data),” J. Penginderaan Jauh dan Pengolah. Data Citra Digit., vol. 14, no. 2, pp. 111–130, 2018. [20] M. N. Fathoni, G. A. Chulafak, and D. Kushardono, “Kajian Awal Pemanfaatan Data Radar Sentinel-1 untuk Pemetaan Lahan Baku Sawah di Kabupaten Indramayu Jawa Barat,” Semin. Nas. Penginderaan jauh ke-4, no. October, pp. 179–186, 2017. [21] J. C. Thouret, Z. Kassouk, A. Gupta, S. C. Liew, and A. Solikhin, “Tracing the evolution of 2010 Merapi volcanic deposits (Indonesia) based on object-oriented classification and analysis of multi-temporal, very high resolution images,” Remote Sens. Environ., vol. 170, pp. 350–371, 2015. [22] I. K. Noviyanti et al., “ANALISIS KETERSEDIAAN RUANG TERBUKA HIJAU DENGAN NDVI MENGGUNAKAN CITRA SATELIT WORLDVIEW-2 DI KOTA ( Analysis of The Availability of Green Open Space with NDVI using Worldview-2 in Yogyakarta City ),” no. 26, pp. 63–70, 2019. [23] A. Elmes et al., “Accounting for training data error in machine learning applied to earth observations,” Remote Sens., vol. 12, no. 6, pp. 1–39, 2020. [24] H. Wulansari, “Uji Akurasi Klasifikasi Penggunaan Lahan dengan Menggunakan Metode Defuzzifikasi Maximum Likelihood Berbasis Citra Alos Avnir-2,” BHUMI J. Agrar. dan Pertanah., vol. 3, no. 1, p. 98, 2017. [25] M. S. Rini, “Kajian kemampuan metode neural network untuk klasifikasi penutup lahan dengan menggunakan Citra Landsat-8 OLI (kasus di Kota Yogyakarta dan sekitarnya),” Geomedia Maj. Ilm. dan Inf. Kegeografian, vol. 16, no. 1, pp. 1–12, 2018. [26] J. Holloway and K. Mengersen, “Statistical machine learning methods and remote sensing for sustainable development goals: A review,” Remote Sens., vol. 10, no. 9, 2018. [27] M. Schmidt, M. Pringle, R. Devadas, R. Denham, and D. Tindall, “A framework for large-area mapping of past and present cropping activity using seasonal landsat images and time series metrics,” Remote Sens., vol. 8, no. 4, 2016. [28] R. A. A. L. M. Agris and R. A. B. Arreto, “Mapping and assessment of protection of mangrove habitats in Brazil,” Panam. J. Aquat. Sci., vol. 5, no. Ong 1995, pp. 546–556, 2010. [29] A. Mellor, “The impact of training data characteristics on ensemble classification of land cover A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy,” RMIT University, 2017.
URL	:	https://doi.org/10.1117/12.2619493