This thesis, co-funded by the ADEME, takes place in the context of a collaboration between the LIRIS laboratory and the Centre Léon Bérard as part of the TESTIS epidemiological study. The TESTIS study aims to estimate the impact of pesticides on the development of germ cell tumor of testicular cancer. As this disease has a long development time, it is necessary to have access to data dating back to the birth of the subjects. In the case of TESTIS, the oldest subjects were born in the early 1970s. In order to take into account individual residential exposures to pesticides spread by winds, the Centre Léon Bérard has developed a metric based on land use around dwellings. Unfortunately no land use database before 1990 is sufficiently accurate to be used. In order to obtain this information, the geomatics specialists at the Centre Léon Bérard are tasked with photo-interpreting historical aerial images in grayscale. This manual process is particularly long and tedious. Therefore, the use of automatic or semi-automatic methods has been suggested. The objective of this thesis is to develop algorithms to help geomatics specialists obtain land cover maps in a reasonable time. For that, we were interested in the use of texture classification methods that we have integrated into an annotation assistance software. This software is currently used in the TESTIS study. We then put our focus on the development of unsupervised colorization methods to provide alternative visualizations of the historical aerial images. This work also led us to study the interest of the artificially generated colors for land use classification. Finally, we sought to improve the land use maps generated by our software through post-processing methods, paving the way for the development of more efficient pipelines.