Detecção automática de áreas urbanas desfavorecidas usando imagens do Google Earth™ de cidades do semiárido brasileiro

Pereira, Eanes Torres; Barros Filho, Mauro Normando Macêdo; Simões, Matheus Batista; Bezerra Neto, José Augusto

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Special Section: Informality in Urban Peripheries • urbe, Rev. Bras. Gest. Urbana 14 • 2022 • https://doi.org/10.1590/2175-3369.014.e20210209 copiar

Detecção automática de áreas urbanas desfavorecidas usando imagens do Google Earth™ de cidades do semiárido brasileiro

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

A classificação automática de áreas urbanas desfavorecidas fornece informações vitais para a implementação de políticas pró-pobres. Neste artigo, é apresentada uma abordagem para a classificação dessas áreas nas cidades brasileiras. As imagens de satélite foram obtidas de modo gratuito para seis cidades do Semiárido brasileiro utilizando o software Google Earth Engine. Para avaliar o poder discriminativo dos dados censitários, foram utilizados dados disponibilizados publicamente pelo Instituto Brasileiro de Geografia e Estatística (IBGE) para treinar classificadores SVM juntamente com características extraídas de imagens. As características da imagem foram extraídas usando as seguintes abordagens: histogramas de cores, histogramas de LBP e lacunaridade. Quatro testes de avaliação foram investigados com base em dois critérios: uso de dados censitários e método de validação cruzada. Foram utilizados dois tipos de validação cruzada: 10 vezes e deixar uma cidade de fora. O uso de dados censitários causou impacto negativo nos resultados. Esse impacto é justificado pelos critérios sobre os quais os setores censitários são mapeados no país, não apenas morfológicos e visualmente perceptíveis por meio de imagens de satélite, ao contrário das abordagens de extração adotadas. Os melhores resultados obtidos foram acurácia média de 91,81% e F1-score médio de 92,27%. Essa pesquisa contribui para o reconhecimento de áreas urbanas precárias e de dinâmicas socioespaciais urbanas, dando suporte ao planejamento urbano-territorial.

Palavras-chave:
Áreas urbanas desfavorecidas; Sensoriamento remoto; Aprendizado de máquina

City	POP (Urban)	POP (Rural)	UA	DP	HDI	GDPPC	GI	BIO
1	367.209	18.004	42.920	8.900	0.720	21,077.300	0.586	Caatinga
2	279.589	35.323	80.561	3.900	0.677	19,311.060	0.542	Caatinga - Atl. Forest
3	510.635	46.007	72.404	7.700	0.712	21,765.410	0.608	Caatinga - Atl. Forest
4	237.241	22.574	11.5834	23.000	0.720	20,858.330	0.534	Caatinga
5	181.481	32.525	8.687	24.000	0.649	17,511.690	0.559	Caatinga - Atl. Forest
6	240.128	9.811	74.400	3.300	0.694	16.375.010	0.549	Caatinga

Author	Feature	Classifier	City	ATS PR		SR	Metric	Result
Kit et al. (2012)Kit, O., Lüdeke, M., & Reckien, D. (2012). Texture-based identification of urban slums in Hyderabad, India using remote sensing data. Applied Geography (Sevenoaks, England), 32(2), 660-667. http://dx.doi.org/10.1016/j.apgeog.2011.07.016. http://dx.doi.org/10.1016/j.apgeog.2011....	Lacunarity	—	Hyderabad, India	—	—	—	—	—
Owen and Wong (2013)Owen, K., & Wong, D. (2013). An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics. Applied Geography, 38, 107-118. http://dx.doi.org/10.1016/j.apgeog.2012.11.016. http://dx.doi.org/10.1016/j.apgeog.2012....	Informality indicators	DFA + CART	Guatemala	118	—	30m	Accuracy	87.5%
Ribeiro (2015)Ribeiro, B. (2015). Mapping informal settlements using WorldView- 2 imagery and C4.5 decision tree classifier. In Joint Urban Remote Sensing Event (pp. 1-4). Crete: JURSE.	GEOBIA	C4.5	Embu-SP (Brazil)	225	—	0.5m and 2.0m	Accuracy and Kappa	90.94% and 88.98%
Kuffer et al. (2016)Kuffer, M., Pfeffer, K., Sliuzas, R., & Baud, I. (2016). Extraction of slum areas from VHR imagery using GLCM variance. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 1830-1840. http://dx.doi.org/10.1109/JSTARS.2016.2538563. http://dx.doi.org/10.1109/JSTARS.2016.25...	GLCM and NDVI	Random Forest	Mumbai, Ahmedabad and Kigali	—	—	From 0.5m to 2.4m	Accuracy and Kappa	Accuracy: 90%; Kappa: 87%
Persello and Stein (2017)Persello, C., & Stein, A. (2017). Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images. IEEE Geoscience and Remote Sensing Letters, 14(12), 2325-2329. http://dx.doi.org/10.1109/LGRS.2017.2763738. http://dx.doi.org/10.1109/LGRS.2017.2763...	Automatic convolutional features	Deep convolutional neural networks	Dar es Salaam	—	125px x 125px	0.6m	Average Accuracy	81.74%
Mboga et al. (2017)Mboga, N., Persello, C., Bergado, J., & Stein, A. (2017). Detection of informal settlements from VHR satellite images using convolutional neurais networks. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5169-5172). New York: IEEE.	Automatic Extraction	CNN+MLP	Dar es Salaam (Tanzania)	3060	160px x 160px	0.60m	Accuracy	90%
Gadiraju et al. (2018)Gadiraju, K., Vatsavai, R., Kaza, N., Wibbels, E., & Krishna, A. (2018). Machine learning approaches for slum detection using very high resolution satellite images. In IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 1397-1404). New York: IEEE. http://dx.doi.org/10.1109/ICDMW.2018.00198. http://dx.doi.org/10.1109/ICDMW.2018.001...	Haralick Texture Features, NDBI (Normalized Difference Built-Up Index), Edge Density and Pansharpened Bands	CNN, Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi Layer Perceptron, Gradient Boosting, Random Forest and Adaboost Classifier	Bengaluru, India	—	40px x 40px	0.5m and 2.0m	Accuracy, Precision, Recall and F-Measure	100%, 86%, 72%
			Bengaluru, India	—	40px x 40px	0.5m and 2.0m	Accuracy, Precision, Recall and F-Measure	100%, 86%, 72%
Ibrahim et al. (2019)Ibrahim, R., Titheridge, H., Cheng, T., & Haworth, J. (2019). predictSLUMS: a new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning. Computers, Environment and Urban Systems, 76, 31-56. http://dx.doi.org/10.1016/j.compenvurbsys.2019.03.005. http://dx.doi.org/10.1016/j.compenvurbsy...	incident points	MLP ANN	4 cities of Egypt and 1 city of India	—	—	—	Accuracy	98%
Proposed leave-one-city-out	LBP, color histograms, lacunarity	SVM	6 Brazilian cities: Arapiraca, Campina Grande, Caruaru, Feira de Santana, Juazeiro do Norte e Mossoró	3406	40px x 40px	10m	Accuracy, recall, precision, F1-score	87.39%, 88.79%, 86.73%, 85.73%
Proposed 10-fold-cross-validation	LBP, color histograms, lacunarity	SVM	6 Brazilian cities: Arapiraca, Campina Grande, Caruaru, Feira de Santana, Juazeiro do Norte e Mossoró	3406	40px x 40px	10m	Accuracy, recall, precision, F1-score	87.22%, 91.81%, 97.95%, 91.81%

City		ACC	PRMI	PRMA	F1	F1MI	F1MA	REMI	REMA
1		78.96	78.96	79.10	79.67	78.96	78.93	78.96	78.96
2		81.45	81.45	81.46	81.62	81.45	81.44	81.45	81.45
3		82.29	82.29	82.42	82.83	82.29	82.27	82.29	82.29
4		57.02	57.02	57.03	57.76	57.02	57.01	57.02	57.02
5		78.75	78.75	78.97	77.78	78.75	78.71	78.75	78.75
6		68.27	68.27	73.76	58.23	68.27	66.32	68.27	68.27
	Av(%)	74.46	74.46	75.46	72.98	74.46	74.11	74.46	74.46
	Std(%)	09.91	09.91	09.51	11.74	09.91	10.18	09.91	09.91

City		ACC	PRMI	PRMA	F1	F1MI	F1MA	REMI	REMA
1		87.21	87.21	89.14	88.48	87.21	87.05	87.21	87.21
2		89.06	89.06	89.06	89.02	89.06	89.06	89.06	89.06
3		91.67	91.67	92.86	92.31	91.67	91.61	91.67	91.67
4		69.04	69.04	77.88	56.92	69.04	66.38	69.04	69.04
5		94.06	94.06	94.11	93.97	94.06	94.06	94.06	94.06
6		93.27	93.27	94.07	93.69	93.27	93.24	93.27	93.27
	Av(%)	87.39	87.39	89.52	85.73	87.39	86.90	87.39	87.39
	Std(%)	09.35	09.35	06.14	14.30	09.35	10.39	09.35	09.35

Fold	COMP	ACC	PRMA	PRMI	F1	F1MI	F1MA	REMI	REMA
0	536	83.58	83.58	84.98	85.79	83.58	83.17	83.58	82.94
1	537	87.68	87.68	87.77	87.35	87.68	87.67	87.68	87.96
2	534	87.68	87.68	88.00	88.59	87.68	87.61	87.68	87.51
3	538	84.75	84.75	85.12	85.39	84.75	84.72	84.75	84.80
4	538	83.87	83.87	84.29	84.68	83.87	83.83	83.87	83.89
5	535	85.92	85.92	86.30	86.21	85.92	85.92	85.92	86.13
6	535	86.47	86.47	87.15	87.15	86.47	86.43	86.47	86.59
7	533	88.53	88.53	89.04	89.20	88.53	88.49	88.53	88.50
8	537	87.94	87.94	88.71	88.89	87.94	87.85	87.94	87.82
9	538	87.65	87.65	88.02	88.40	87.65	87.60	87.65	87.56
Av(%)	-	86.41	86.41	86.93	87.16	86.40	86.32	86.40	86.37
Std(%)	-	01.80	01.80	01.67	01.59	01.80	01.86	01.80	01.90

Fold	COMP	ACC	PRMA	PRMI	F1	F1MI	F1MA	REMI	REMA
0	119	92.08	92.08	92.60	92.22	92.08	92.08	92.08	92.40
1	119	92.08	92.08	92.57	92.60	92.08	92.04	92.08	91.98
2	119	90.32	90.32	91.11	90.81	90.32	90.30	90.32	90.47
3	119	92.38	92.38	92.90	92.57	92.38	92.37	93.80	92.61
4	119	90.91	90.91	91.99	92.07	90.91	90.71	90.91	90.31
5	118	93.26	93.26	93.86	93.70	93.26	93.22	93.26	93.17
6	119	91.18	91.18	91.60	91.12	91.18	91.18	91.18	91.67
7	119	91.47	91.47	92.24	92.01	91.47	91.43	91.47	91.47
8	119	91.18	91.18	91.58	91.67	91.18	91.15	91.18	91.12
9	119	93.24	93.24	93.87	93.90	93.24	93.15	93.24	92.92
Av(%)	-	91.81	91.81	92.43	92.27	91.81	91.76	91.81	91.81
Std(%)	-	00.97	00.97	00.93	00.99	00.97	00.98	00.97	00.99

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[1] ETP is doctor in Computer Science, e-mail: eanes@computacao.ufcg.edu.br

MNMBF is doctor in Urban Development, e-mail: mbarrosfilho@gmail.com

MBS is architect and urbanist, e-mail: matheus.simoes@ufpe.br

JABN is bachelor of Computer Science, e-mail: jose.augusto.neto@ccc.ufcg.edu.br