Water　is　a　vital　component　of　life　that　exists　in　a　variety　of　forms,　including　oceans,　rivers,　ponds,　streams,　and　canals.　The　automated　methods　for　detecting,　segmenting,　and　mapping　surface　water　have　improved　significantly　with　the　advancements　in　satellite　imagery　and　remote　sensing.　Many　strategies　and　techniques　to　segment　water　resources　have　been　presented　in　the　past.　However,　due　to　the　variant　width　and　complex　appearance,　the　segmentation　of　the　water　channel　remains　challenging.　Moreover,　traditional　supervised　deep　learning　frameworks　have　been　restricted　by　the　scarcity　of　water　channel　datasets　that　include　precise　water　annotations.　With　this　in　mind,　this　research　presents　the　following　three　main　contributions.　Firstly,　we　curated　a　new　dataset　for　water　channel　mapping　in　the　Pakistani　region.　Instead　of　employing　pixel-level　water　channel　annotations,　we　used　a　weakly　trained　method　to　extract　water　channels　from　VHR　pictures,　relying　only　on　OpenStreetMap　(OSM)　waterways　to　create　sparse　scribbling　annotations.　Secondly,　we　benchmarked　the　dataset　on　state-of-the-art　semantic　segmentation　frameworks.　We　also　proposed　AUnet,　an　atrous　convolution　inspired　deep　learning　network　for　precise　water　channel　segmentation.　The　experimental　results　demonstrate　the　superior　performance　of　the　proposed　AUnet　model　for　segmenting　using　weakly　supervised　labels,　where　it　achieved　a　mean　intersection　over　union　score　of　0.8791　and　outperformed　state-of-the-art　approaches　by　5.90%　for　the　extraction　of　water　channels.