Aberration-corrected　annular　dark-field　scanning　transmission　electron　microscopy　(ADF-STEM)　is　a　powerful　tool　for　structural　and　chemical　analysis　of　materials.　Conventional　analyses　of　ADF-STEM　images　rely　on　human　labeling,　making　them　labor-intensive　and　prone　to　subjective　error.　Here,　we　introduce　a　deep-learning-based　workflow　combining　a　pix2pix　network　for　image　denoising　and　either　a　mathematical　algorithm　local　intensity　threshold　segmentation　(LITS)　or　another　deep　learning　network　UNet　for　chemical　identification.　After　denoising,　the　processed　images　exhibit　a　five-fold　improvement　in　signal-to-noise　ratio　and　a　20%　increase　in　accuracy　of　atomic　localization.　Then,　we　take　atomic-resolution　images　of　Y-Ce　dual-atom　catalysts　(DACs)　and　Fe-doped　ReSe2　nanosheets　as　examples　to　validate　the　performance.　Pix2pix　is　applied　to　identify　atomic　sites　in　Y-Ce　DACs　with　a　location　recall　of　0.88　and　a　location　precision　of　0.99.　LITS　is　used　to　further　differentiate　Y　and　Ce　sites　by　the　intensity　of　atomic　sites.　Furthermore,　pix2pix　and　UNet　workflow　with　better　automaticity　is　applied　to　identification　of　Fe-doped　ReSe2　nanosheets.　Three　types　of　atomic　sites　(Re,　the　substitution　of　Fe　for　Re,　and　the　adatom　of　Fe　on　Re)　are　distinguished　with　the　identification　recall　of　more　than　0.90　and　the　precision　of　higher　than　0.93.　These　results　suggest　that　this　strategy　facilitates　high-quality　and　automated　chemical　identification　of　atomic-resolution　images.