added readme

README.md

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-# GeoEstimation
+# Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification
+This is the official GitHub page for the paper:
+
+> Eric Müller-Budack, Kader Pustu-Iren, Ralph Ewerth:
+"Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification".
+Forthcoming: *European Conference on Computer Vision (ECCV).* Munich, 2018.
+
+# Content
+
+This repository contains:
+* Meta information for the MP-16 training dataset (TODO) as well as the
+Im2GPS ([im2gps_places365.csv](im2gps_places365.csv)) and Im2GPS3k ([im2gps3k_places365.csv](im2gps3k_places365.csv))
+test datasets:
+    - Relative image path containing the Flickr-ID
+    - Flickr Author-ID
+    - Ground-truth latitude
+    - Ground-truth longitude
+    - Predicted scene label in *S_3*
+    - Predicted scene label in *S_16*
+    - Predicted scene label in *S_365*
+    - Probability for *S_3* concept *indoor*
+    - Probability for *S_3* concept *natural*
+    - Probability for *S_3* concept *urban*
+* List of geographical cells for all partitionings:
+    - coarse: [cells_50_1000.csv](cells_50_1000.csv)
+    - middle: [cells_50_2000.csv](cells_50_2000.csv)
+    - fine: [cells_50_5000.csv](cells_50_5000.csv)
+* Results for the reported approaches on both test datasets <approach_parameters.csv>:
+    - Relative image path containing the Flickr-ID
+    - Ground-truth latitude
+    - Predicted latitude
+    - Ground-truth longitude
+    - Predicted longitude
+    - Great-circle distance (GCD)
+
+# Images
+
+The (list of) image files for training and testing can be found on the following links:
+* MP-16: http://multimedia-commons.s3-website-us-west-2.amazonaws.com/
+* Im2GPS: http://graphics.cs.cmu.edu/projects/im2gps/
+* Im2GPS-3k: https://github.com/lugiavn/revisiting-im2gps
+
+# Geographical Cell Partitioning
+
+The geographical cell labels are extracted using the *S2 geometry library*:
+https://code.google.com/archive/p/s2-geometry-library/
+
+The python implementation *s2sphere* can be found on:
+http://s2sphere.readthedocs.io/en/
+
+The geographical cells can be visualized on:
+http://s2.sidewalklabs.com/regioncoverer/
+
+# Scene Classification
+
+The scene labels and probabilities are extracted using the *Places365 ResNet 152 model* from:
+https://github.com/CSAILVision/places365
+
+In order to generate the labels for the superordinate scene categories the *Places365 hierarchy* is used:
+http://places2.csail.mit.edu/download.html
+
+# Model
+
+All models were trained using TensorFlow
+
+* Baseline approach for middle partitioning: TODO
+* Multi-partitioning baseline approach: TODO
+* Multi-partitioning Individual Scenery Network for *S_3* concept *indoor*: TODO
+* Multi-partitioning Individual Scenery Network for *S_3* concept *natural*: TODO
+* Multi-partitioning Individual Scenery Network for *S_3* concept *urban*: TODO
+
+We are currently working on a deploy source code.
+
+# LICENSE
+
+This work is published under the GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007. For details please check the
+LICENSE file in the repository.