Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

Table 3 Prediction accuracy test results for drain, trash, muddy water (M.Water), water point (W.Point), water bucket (W.Bucket), tires, and animals

Video#	Drain				Trash				M.Water
Video#	T	R1%	R2%	R3%	T	R1%	R2%	R3%	T	R1%	R2%	R3%
1	15	100	100	100	10	100	100	100	9	78	89	89
2	20	95	95	100	19	74	84	100	62	65	77	92
3	13	100	100	100	25	76	84	100	38	74	89	95
4	15	100	100	100	11	64	73	100	6	83	83	100
5	6	50	100	100	15	27	53	93	8	38	38	75
6	7	86	86	100	40	75	85	93	65	83	95	100
7	21	100	100	100	21	57	86	90	31	55	81	97
8	6	100	100	100	7	100	100	100	5	80	100	100
9	4	75	75	75	16	88	94	100	11	45	73	100
10	5	80	100	100	43	72	84	98	31	55	71	87
11	10	100	100	100	17	76	88	94	13	69	85	100
12	13	62	92	92	15	80	80	93	29	72	72	90
Totals	135	91	97	99	239	72	84	96	308	68	82	94

W. Point					W. Bucket				Tire				Animal
T	R1%	R2%	R3%	R4%	T	R1%	R2%	R3%	T	R1%	R2%	R3%	T	R1%	R2%	R3%
4	100	100	100	100	16	88	100	100	1	100	100	100	5	100	100	100
6	50	50	83	83	27	96	96	96	18	83	89	89	7	86	100	100
3	100	100	100	100	11	100	100	100	2	100	100	100	3	67	67	67
7	86	86	86	86	9	100	100	100	1	0	100	100	1	100	100	100
5	60	60	60	80	9	78	78	78	14	93	93	93	7	86	100	100
3	33	33	67	67	4	100	100	100	2	50	50	50	8	100	100	100
4	25	25	100	100	9	100	100	100	25	88	92	92	6	67	83	83
1	100	100	100	100	1	100	100	100	1	0	0	0	1	0	0	0
3	33	33	33	100	2	100	100	100	4	100	100	100	2	100	100	100
5	80	80	80	100	2	100	100	100	3	100	100	100	3	67	100	100
2	100	100	100	100	3	100	100	100					1	0	0	0
8	100	100	100	100	12	100	100	100	7	86	100	100
51	73	73	84	92	105	95	97	97	71	86	91	91	44	82	91	91

ISSN: 1476-072X

W. Point					W. Bucket				Tire				Animal
T	R1%	R2%	R3%	R4%	T	R1%	R2%	R3%	T	R1%	R2%	R3%	T	R1%	R2%	R3%
4	100	100	100	100	16	88	100	100	1	100	100	100	5	100	100	100
6	50	50	83	83	27	96	96	96	18	83	89	89	7	86	100	100
3	100	100	100	100	11	100	100	100	2	100	100	100	3	67	67	67
7	86	86	86	86	9	100	100	100	1	0	100	100	1	100	100	100
5	60	60	60	80	9	78	78	78	14	93	93	93	7	86	100	100
3	33	33	67	67	4	100	100	100	2	50	50	50	8	100	100	100
4	25	25	100	100	9	100	100	100	25	88	92	92	6	67	83	83
1	100	100	100	100	1	100	100	100	1	0	0	0	1	0	0	0
3	33	33	33	100	2	100	100	100	4	100	100	100	2	100	100	100
5	80	80	80	100	2	100	100	100	3	100	100	100	3	67	100	100
2	100	100	100	100	3	100	100	100					1	0	0	0
8	100	100	100	100	12	100	100	100	7	86	100	100
51	73	73	84	92	105	95	97	97	71	86	91	91	44	82	91	91

W. Point					W. Bucket				Tire				Animal
T	R1%	R2%	R3%	R4%	T	R1%	R2%	R3%	T	R1%	R2%	R3%	T	R1%	R2%	R3%
4	100	100	100	100	16	88	100	100	1	100	100	100	5	100	100	100
6	50	50	83	83	27	96	96	96	18	83	89	89	7	86	100	100
3	100	100	100	100	11	100	100	100	2	100	100	100	3	67	67	67
7	86	86	86	86	9	100	100	100	1	0	100	100	1	100	100	100
5	60	60	60	80	9	78	78	78	14	93	93	93	7	86	100	100
3	33	33	67	67	4	100	100	100	2	50	50	50	8	100	100	100
4	25	25	100	100	9	100	100	100	25	88	92	92	6	67	83	83
1	100	100	100	100	1	100	100	100	1	0	0	0	1	0	0	0
3	33	33	33	100	2	100	100	100	4	100	100	100	2	100	100	100
5	80	80	80	100	2	100	100	100	3	100	100	100	3	67	100	100
2	100	100	100	100	3	100	100	100					1	0	0	0
8	100	100	100	100	12	100	100	100	7	86	100	100
51	73	73	84	92	105	95	97	97	71	86	91	91	44	82	91	91

W. Point					W. Bucket				Tire				Animal
T	R1%	R2%	R3%	R4%	T	R1%	R2%	R3%	T	R1%	R2%	R3%	T	R1%	R2%	R3%
4	100	100	100	100	16	88	100	100	1	100	100	100	5	100	100	100
6	50	50	83	83	27	96	96	96	18	83	89	89	7	86	100	100
3	100	100	100	100	11	100	100	100	2	100	100	100	3	67	67	67
7	86	86	86	86	9	100	100	100	1	0	100	100	1	100	100	100
5	60	60	60	80	9	78	78	78	14	93	93	93	7	86	100	100
3	33	33	67	67	4	100	100	100	2	50	50	50	8	100	100	100
4	25	25	100	100	9	100	100	100	25	88	92	92	6	67	83	83
1	100	100	100	100	1	100	100	100	1	0	0	0	1	0	0	0
3	33	33	33	100	2	100	100	100	4	100	100	100	2	100	100	100
5	80	80	80	100	2	100	100	100	3	100	100	100	3	67	100	100
2	100	100	100	100	3	100	100	100					1	0	0	0
8	100	100	100	100	12	100	100	100	7	86	100	100
51	73	73	84	92	105	95	97	97	71	86	91	91	44	82	91	91