Fast　and　accurately　categorizing　the　millions　of　aerial　images　on　Google　Maps　is　a　useful　technique　in　pattern　recognition.　Existing　methods　cannot　handle　this　task　successfully　due　to　two　reasons:　1)　the　aerial　images＇　topologies　are　the　key　feature　to　distinguish　their　categories,　but　they　cannot　be　effectively　encoded　by　a　conventional　visual　codebook　and　2)　it　is　challenging　to　build　a　realtime　image　categorization　system,　as　some　geo-aware　Apps　update　over　20　aerial　images　per　second.　To　solve　these　problems,　we　propose　an　efficient　aerial　image　categorization　algorithm.　It　focuses　on　learning　a　discriminative　topological　codebook　of　aerial　images　under　a　multitask　learning　framework.　The　pipeline　can　be　summarized　as　follows.　We　first　construct　a　region　adjacency　graph　(RAG)　that　describes　the　topology　of　each　aerial　image.　Naturally,　aerial　image　categorization　can　be　formulated　as　RAG-to-RAG　matching.　According　to　graph　theory,　RAG-to-RAG　matching　is　conducted　by　enumeratively　comparing　all　their　respective　graphlets　(i.e.,　small　subgraphs).　To　alleviate　the　high　time　consumption,　we　propose　to　learn　a　codebook　containing　topologies　jointly　discriminative　to　multiple　categories.　The　learned　topological　codebook　guides　the　extraction　of　the　discriminative　graphlets.　Finally,　these　graphlets　are　integrated　into　an　AdaBoost　model　for　predicting　aerial　image　categories.　Experimental　results　show　that　our　approach　is　competitive　to　several　existing　recognition　models.　Furthermore,　over　24　aerial　images　are　processed　per　second,　demonstrating　that　our　approach　is　ready　for　real-world　applications.