# Revision history [back]

### LandscapeClassifier and Geographic Locations

Kathrine, the answer explained many things, but I hope I am not getting repetitive at this point. Let me try to have a more detailed explanation:

I have a trained image dataset: trees-folder, roses-folder and house-folder. I have an test image dataset: folder1, folder2, folder3, folder4, folder_5

I know from my research that images in the five folders belong to those object classes (trees, roses, houses), but they are disordered. For example I do not know how many trees, roses or houses are in folder2. This step is very important, because

1. the images in the folders (1-5) are grouped according a specific geographic location and I want to know how many object classes are present in a specific geographic location. Answering the question on how many trees, roses or houses are present in folder4, discloses information on the geographic location represented by folder4.

2. the task of identifying objects in the folders was (at the beginning of the research) empirically defined by an expert (a human being) for each of the test folders. In other words I want to be SimpleCV my expert and therefore enabling me to (partly) automate this classification process. Ideally in this way:

         trees  roses  houses
folder_1
folder_2
folder_3
folder_4
folder_5


Just a suggestion, as you explained in your answer, has it sense to label each folder in test dataset in the same way as the trained dataset, regardless of the content of the folder? I mean, for me it is not important to know how the folder is labeled, its just important to keep track on how I re-labeled my folders. Can this be a sort of work around?

                          trees  roses  houses
trees (former folder_1)
roses (former folder_2)
houses (former folder_3)
and so on...


thanks!