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Photo Geolocation Estimation On Im2Gps

Metrics

City level (25 km)

Continent level (2500 km)

Country level (750 km)

Median Error (km)

Reference images

Region level (200 km)

Street level (1 km)

Training images

Results

Performance results of various models on this benchmark

									Paper Title	Repository
ISNs (M, f*, S3)	43.0	80.2	66.7	-	0	51.9	16.9	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
PIGEOTTO	40.9	91.1	82.3	70.5	4.5M	63.3	14.8	4.5M	PIGEON: Predicting Image Geolocations
base (M, f*)	40.9	78.5	65.4	-	0	51.5	15.2	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
CPlaNet (1-5, PlaNet)	37.1	78.5	62.0	-	0	46.6	16.5	30.3M	CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps	-
base (L, m)	35.0	79.7	64.1	-	0	49.8	13.5	4.7M	Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification	-
Im2GPS ([L] KNN, sigma=4)	33.3	71.3	57.4	-	0	44.3	12.2	6M	Revisiting IM2GPS in the Deep Learning Era	-
Im2GPS (... 28m database)	33.3	73.4	61.6	-	28M	47.7	14.4	6M	Revisiting IM2GPS in the Deep Learning Era	-
StreetCLIP (Zero-Shot)	28.3	88.2	74.7	-	0	45.1	-	1.1M	Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
PlaNet (91M)	24.5	71.3	53.6	-	0	37.6	8.4	91M	PlaNet - Photo Geolocation with Convolutional Neural Networks
Im2GPS ([L] 7011C)	21.9	63.7	49.4	-	0	34.6	6.8	6M	Revisiting IM2GPS in the Deep Learning Era	-
PlaNet (6.2M)	18.1	65.8	45.6	-	0	30.0	6.3	6.2M	PlaNet - Photo Geolocation with Convolutional Neural Networks

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Photo Geolocation Estimation On Im2Gps | SOTA | HyperAI