Automatic extraction of built-up areas in Chinese urban agglomerations based on the deep learning method using NTL data

Figures & data

Figure 1. The study area: (a) Central Plains Urban Agglomeration and (b) Shandong Peninsula Urban Agglomeration.

Figure 2. Overall process of the proposed method.

Figure 3. The deep neural network architecture applied in this study.

Table 1. Descriptions of landscape pattern indicators.

Type	Calculation formula	Value range	Unit	Description
Total patch area	$\mathit{TPA}={\sum }_{i=1}^n{a}_i$	TPA > 0	km^2	The total area of built-up patches
Number of patches	$NP=n$	NP > 0		The number of patches can reflect the fragmentation of the built-up patches and has a good positive correlation with the fragmentation degree of the built-up patches.
Average patch area	$\mathit{APA}=\frac{{\sum }_{i=1}^n{a}_i}{n}$	$\mathit{APA}$>0	km^2	The average patch area can reflect the average size of built-up patches.
Patch density	$PD=\frac{n}{A}\times {10}^8$	PD > 0	per 100 km^2	The patch density refers to the number of patches per 100 km2 and can reflect the density of built-up patches.
Largest patch index	$\mathit{LPI}=\frac{\mathrm{Max}(a_1,a_2,\dots ,a_n)}{A}\times 100$	0 < LPI $\le$100	%	The largest patch index is the percentage of the largest built-up patch area in the total area of the study area, indicating the impact of the largest built-up patch on the study area.
Total edge length	$\mathit{TEL}={\sum }_{i=1}^n{e}_i$	TEL > 0	km	The total edge length of all built-up patches in the study area
Average edge density	$\mathit{AED}=\frac{{\sum }_{i=1}^n{e}_i}{A}\times {10}^8$	AED $\ge$0	km per 100 km^2	The average edge density can reflect the degree of dividing the study area by the built-up patches’ boundary.
Landscape shape index	$\mathit{LSI}=\frac{0.25{\sum }_{i=1}^n{e}_i}{\sqrt{A}}$	LSI ≥ 1		The landscape shape index can reflect the shape change of the built-up patches. The larger the value is, the more complex the patch shape is.
Aggregation index	$AI=\left[\frac{g_{ii}}{\max \to g_{ii}}\right]\times 100$	0 ≤ AI ≤ 100	%	The aggregation index can reflect the connectivity between the built-up patches. The smaller the value is, the more discrete the patches are.

Notes: n represents the total numbers of the built-up patches, $a_i$ represents the area of the i-th built-up patch (${\mathrm{m}}^2),$ A represents the total area of the study area (${\mathrm{m}}^2),$ $\mathrm{Max}(a_1,a_2,\dots ,a_n)$ represents the area of the largest built-up patch in the study area (${\mathrm{m}}^2),$ $e_i$ represents the edge length of the i-th built-up patch ($km$), $g_{ii}$ represents the connectivity number between the built-up patches, and $\max \to g_{ii}$ represents the maximum connectivity number between the built-up patches.

Table 2. The confusion matrix.

	Built-up area (ground truth)	Nonbuilt-up area (ground truth)
Built-up area (prediction)	TP	FP
Nonbuilt-up area (Prediction)	FN	TN

Table 3. Comparison of different methods.

Method	Precision	Recall	F1 score	IoU
Our method	0.9160	0.8642	0.8894	0.8008
BAEM method	0.8441	0.8612	0.8526	0.7430

Figure 5. Changes in the built-up areas in the study area from 2000 to 2020: (a) Central Plains Urban Agglomeration and (b) Shandong Peninsula Urban Agglomeration.

Table 4. The statistics of the gravity centre migration of the built-up areas in the Central Plains Urban Agglomeration.

Year	X coordinate (m)	Y coordinate (m)	Migration distance (km)	Migration direction (degree)	Remarks
2000	813287.41	3750585.42			The gravity centre of the built-up areas in the previous year was set as the origin of the coordinate axis, and the following coordinate system of the migration direction was established. Notes: The Y axis is the north direction, and the X axis is the east direction.
2001	809663.72	3765045.85	14.91	104.07
2002	805639.48	3748628.10	16.90	−103.77
2003	812316.54	3740823.85	10.27	−49.45
2004	809719.51	3742417.02	3.05	148.47
2005	803127.77	3738681.01	7.58	−150.46
2006	811778.70	3735797.50	9.12	−18.43
2007	821749.30	3726795.12	13.43	−42.08
2008	815813.28	3723487.25	6.80	−150.87
2009	821691.83	3737095.59	14.82	66.64
2010	826950.46	3725605.76	12.64	−65.41
2011	829890.45	3721627.84	4.95	−53.53
2012	829678.92	3727594.77	5.97	92.03
2013	829521.63	3716064.03	11.53	−90.78
2014	837365.63	3716242.52	7.85	1.30
2015	841705.35	3714279.95	4.76	−24.33
2016	842490.85	3713648.03	1.01	−38.82
2017	841667.76	3712200.30	1.67	−119.62
2018	841869.40	3711005.38	1.21	−80.42
2019	840856.85	3712657.74	1.94	121.50
2020	839869.74	3713138.12	1.10	154.05

Table 5. The statistics of the gravity centre migration of the built-up areas in the Shandong Peninsula Urban Agglomeration.

Year	X coordinate (m)	Y coordinate (m)	Migration distance (km)	Migration direction (degree)	Remarks
2000	1184962.75	3966467.83			The gravity centre of the built-up areas in the previous year was set as the origin of the coordinate axis, and the following coordinate system of the migration direction was established. Notes: The Y axis is the north direction, and the X axis is the east direction.
2001	1187833.72	3970144.15	4.66	52.01
2002	1180253.30	3964134.97	9.67	−141.60
2003	1187336.91	3966743.51	7.55	20.22
2004	1182284.83	3961566.14	7.23	−134.30
2005	1183046.87	3962406.31	1.13	47.79
2006	1189501.68	3961308.70	6.55	−9.65
2007	1192738.75	3960306.47	3.39	−17.20
2008	1193096.84	3961502.66	1.25	73.33
2009	1184525.51	3957356.73	9.52	−154.19
2010	1183208.81	3958405.37	1.68	141.47
2011	1182604.38	3957872.33	0.81	−138.59
2012	1174984.17	3955409.34	8.01	−162.09
2013	1177924.01	3956889.60	3.29	26.73
2014	1176144.51	3955113.99	2.51	−135.06
2015	1177952.45	3955068.48	1.81	−1.44
2016	1177911.58	3955161.81	0.10	113.65
2017	1174734.31	3953310.67	3.68	−149.77
2018	1176158.91	3953562.81	1.45	10.04
2019	1172289.24	3953400.81	3.87	−177.60
2020	1172814.07	3952513.07	1.03	−59.41

Figure 6. The gravity centre migration trajectories of the built-up areas in the study area from 2000 to 2020: (a) Central Plains Urban Agglomeration and (b) Shandong Peninsula Urban Agglomeration.

Table 6. The calculation results of the landscape pattern indicators of the built-up areas in the Central Plains Urban Agglomeration.

Year	TPA (km^2)	NP	PD (per 100 km^2)	LPI (%)	TEL (km)	AED (km per 100 km^2)	LSI	AI (%)
2000	1665.50	328	0.11	0.0314	3236.0	1.132	19.7378	76.6489
2001	1847.50	336	0.12	0.0490	3373.0	1.180	19.6163	78.0805
2002	2599.00	414	0.14	0.0680	4576.0	1.601	22.4657	78.7303
2003	2712.00	396	0.14	0.0781	4679.0	1.637	22.3971	79.1874
2004	3558.00	464	0.16	0.0767	6154.0	2.153	25.7741	79.0221
2005	3508.00	442	0.15	0.0838	5852.0	2.047	24.7089	79.8074
2006	3638.75	433	0.15	0.0984	5743.5	2.009	23.7479	80.9304
2007	3577.75	474	0.17	0.0868	6005.0	2.101	25.0333	79.6773
2008	3787.75	471	0.16	0.1125	6174.0	2.160	24.9960	80.2795
2009	3736.00	509	0.18	0.0836	6451.0	2.257	26.3306	79.0642
2010	4203.50	617	0.22	0.1014	7639.5	2.672	29.3923	77.8770
2011	4976.50	654	0.23	0.1438	7997.0	2.798	28.2933	80.4599
2012	4944.50	905	0.32	0.0937	8890.5	3.110	31.5496	78.0644
2013	6387.25	1240	0.43	0.1374	11448.0	4.005	35.8563	78.0338
2014	6730.00	1125	0.39	0.1439	11164.5	3.906	34.0213	79.6976
2015	6771.75	1092	0.38	0.1445	11146.5	3.899	33.8424	79.8715
2016	6827.00	1095	0.38	0.1474	11417.0	3.994	34.5559	79.5395
2017	7403.25	1137	0.40	0.1795	11955.5	4.182	34.7217	80.2415
2018	7771.00	1166	0.41	0.1957	12519.5	4.380	35.5411	80.2750
2019	8198.00	1211	0.42	0.1867	13049.5	4.565	36.0165	80.5109
2020	8568.25	1303	0.46	0.2043	13836.0	4.840	37.3450	80.2215
Annual growth rate	8.53%	7.14%	7.42%	9.82%	7.54%	7.54%	3.24%	8.53%

Figure 7. The changes in the typical landscape pattern indicators in the six main regions of the Central Plains Urban Agglomeration: (a) TPA, (b) NP, (c) APA, (d) LSI, and (e) AI.

Table 7. The calculation results of the landscape pattern indicators of the built-up areas in the Shandong Peninsula Urban Agglomeration.

Year	TPA (km^2)	NP	PD (per 100 km^2)	LPI (%)	TEL (km)	AED (km per 100 km^2)	LSI	AI (%)
2000	1828.00	205	0.13	0.0906	2911.0	1.868	17.1860	80.7362
2001	2149.75	250	0.16	0.1126	3442.0	2.208	18.7634	80.5784
2002	2463.00	314	0.20	0.1030	4133.0	2.652	21.0804	79.5129
2003	2844.50	338	0.22	0.1163	4645.0	2.980	22.0280	80.0373
2004	3348.75	386	0.25	0.1156	5651.5	3.626	24.7328	79.2680
2005	3467.50	367	0.24	0.1193	5584.5	3.583	23.9873	80.2756
2006	3597.75	385	0.25	0.1678	5775.0	3.705	24.3958	80.3272
2007	3735.50	334	0.21	0.1647	5425.5	3.481	22.4286	82.2869
2008	3866.50	432	0.28	0.1517	6269.5	4.022	25.5783	80.0541
2009	3901.00	351	0.23	0.1862	5711.5	3.664	23.1560	82.1080
2010	4166.00	580	0.37	0.1468	7703.5	4.942	30.1506	77.1689
2011	4203.75	620	0.40	0.1752	7463.5	4.788	29.0538	78.1420
2012	4316.50	572	0.37	0.1296	7401.5	4.749	28.5437	78.8614
2013	5127.75	957	0.61	0.1660	9603.5	6.161	33.9617	76.7767
2014	5510.75	911	0.58	0.1803	9753.0	6.257	33.3165	78.0813
2015	5530.25	947	0.61	0.1788	9928.5	6.370	33.8154	77.7467
2016	5632.25	940	0.60	0.1894	9856.0	6.323	33.2326	78.3229
2017	6052.25	950	0.61	0.2254	10243.0	6.572	33.3141	79.0421
2018	6288.75	1033	0.66	0.2177	10818.0	6.941	34.4937	78.6946
2019	6441.00	1035	0.66	0.2523	11066.0	7.100	34.7733	78.7623
2020	6814.00	1112	0.71	0.2743	11724.5	7.522	35.8248	78.7250
Annual growth rate	6.8%	8.82%	8.86%	5.7%	7.21%	7.21%	3.74%	−0.13%

Figure 8. The changes in the landscape pattern indicators of the six areas in the Shandong Peninsula Urban Agglomeration from 2000 to 2020: (a) TPA, (b) NP, (c) APA, (d) LSI, and (e) AI.