Geometric　and　semantic　contexts　are　essential　to　solving　the　ill-posed　problem　of　monocular　depth　estimation　(MDE).　In　this　paper,　we　propose　a　deep　MDE　framework　that　can　aggregate　dual-modal　structural　contexts　for　monocular　depth　estimation　(DSC-MDE).　First,　a　cross-shaped　context　(CSC)　aggregation　module　is　developed　to　globally　encode　the　geometric　structures　in　depth　maps　observed　under　the　fields　of　vision　of　robots/autonomous　vehicles.　Next,　the　CSC-encoded　geometric　features　are　further　modulated　with　semantic　context　in　an　object-regional　context　(ORC)　aggregation　module.　Finally,　to　train　the　proposed　network,　we　present　a　focal　ordinal　loss　(FOL),　which　pays　more　attention　to　distant　samples　to　avoid　the　issue　of　over-relaxed　constraints　on　these　samples　occurring　in　the　ordinal　regression　loss　(ORL).　We　compare　the　proposed　model　to　recent　methods　with　geometric　and　multi-modal　contexts,　and　show　that　the　proposed　model　obtains　state-of-the-art　performance　on　both　indoor　and　outdoor　datasets,　including　NYU-Depth-V2,　Cityscapes　and　KITTI.　(c)　2023　Elsevier　B.V.　All　rights　reserved.