To　control　the　negative　effects　resulting　from　the　disorderly　development　of　aquaculture　ponds　and　promote　the　development　of　the　aquaculture　industry,　rapid　and　accurate　identification　and　extraction　techniques　are　essential.　An　aquaculture　pond　is　a　special　net-like　water　body　divided　by　complex　roads　and　dikes.　Simple　spectral　features　or　spatial　texture　features　are　not　sufficient　to　accurately　extract　it,　and　the　mixed　feature　rule　set　is　more　demanding　on　computer　performance.　Supported　by　the　GEE　platform,　and　using　the　Landsat　satellite　data　set　and　corresponding　DEM　combined　with　field　survey　data,　we　constructed　a　decision-making　model　for　the　extraction　of　aquaculture　ponds　in　the　coastal　waters,　and　applied　this　method　to　the　coastal　waters　of　Southeast　China.　This　method　combined　the　image　spectral　information,　spatial　features,　and　morphological　operations.　The　results　showed　that　the　total　accuracy　of　this　method　was　93%,　and　the　Kappa　coefficient　was　0.86.　The　overlapping　proportions　of　results　between　the　automated　extraction　and　visual　interpretation　for　test　areas　were　all　more　than　90%,　and　the　average　was　92.5%,　which　reflected　the　high　precision　and　reliability　of　this　extraction　method.　Furthermore,　in　2020,　the　total　area　of　coastal　aquaculture　ponds　in　the　study　area　was　6348.51　km(2),　which　was　distributed　primarily　in　the　cities　of　Guangdong　and　Jiangsu.　Kernel　density　analysis　suggested　that　aquaculture　ponds　in　Guangdong　and　Jiangsu　had　the　highest　degree　of　concentration,　which　means　that　they　face　higher　regulatory　pressure　in　the　management　of　aquaculture　ponds　than　other　provinces.　Therefore,　this　method　can　be　used　to　extract　aquaculture　ponds　in　coastal　waters　of　the　world,　and　holds　great　significance　to　promote　the　orderly　management　and　scientific　development　of　fishery　aquaculture.