The　processing　and　recognition　of　geoscience　images　have　wide　applications.　Most　of　existing　researches　focus　on　understanding　the　high-quality　geoscience　images　by　assuming　that　all　the　images　are　clear.　However,　in　many　real-world　cases,　the　geoscience　images　might　contain　occlusions　during　the　image　acquisition.　This　problem　actually　implies　the　image　inpainting　problem　in　computer　vision　and　multimedia.　As　far　as　we　know,　all　the　existing　image　inpainting　algorithms　learn　to　repair　the　occluded　regions　for　a　better　visualization　quality,　they　are　excellent　for　natural　images　but　not　good　enough　for　geoscience　images,　and　they　never　consider　the　following　gescience　task　when　developing　inpainting　methods.　This　paper　aims　to　repair　the　occluded　regions　for　a　better　geoscience　task　performance　and　advanced　visualization　quality　simultaneously,　without　changing　the　current　deployed　deep　learning　based　geoscience　models.　Because　of　the　complex　context　of　geoscience　images,　we　propose　a　coarse-to-fine　encoder-decoder　network　with　the　help　of　designed　coarse-to-fine　adversarial　context　discriminators　to　reconstruct　the　occluded　image　regions.　Due　to　the　limited　data　of　geoscience　images,　we　propose　a　MaskMix　based　data　augmentation　method,　which　augments　inpainting　masks　instead　of　augmenting　original　images,　to　exploit　the　limited　geoscience　image　data.　The　experimental　results　on　three　public　geoscience　datasets　for　remote　sensing　scene　recognition,　cross-view　geolocation　and　semantic　segmentation　tasks　respectively　show　the　effectiveness　and　accuracy　of　the　proposed　method.　The　code　is　available　at:　https://github.com/HMS97/Task-driven-Inpainting.