Construction　waste　is　an　inevitable　byproduct　of　urban　renewal　processes,　causing　serious　environmental　pollution　and　ecological　pressure.　Precisely　quantifying　the　annual　production　of　urban　construction　waste　and　the　resource　conversion　rate　is　crucial　for　assessing　the　cost　of　urban　renewal.　Traditional　manual　methods　of　estimating　construction　waste　production　rely　heavily　on　statistical　data　and　historical　experience,　which　are　inflexible,　time-consuming,　and　labor-intensive　in　practical　application,　and　need　improvement　in　terms　of　accuracy　and　timeliness.　Existing　deep　learning　models　have　relatively　poor　capabilities　in　extracting　and　integrating　small　targets　and　multi-scale　features,　making　it　difficult　to　handle　irregular　shapes　and　fragmented　detection　areas.　This　paper　proposes　a　Multi-Scale　Feature　Fusion　and　Attention-Enhanced　Network　(MS-FF-AENet)　based　on　High-resolution　Remote　Sensing　Images　(HRSIs)　to　dynamically　track　and　detect　changes　in　buildings　and　construction　waste　disposal　sites.　This　paper　introduces　a　novel　encoder-decoder　structure,　utilizing　ResNet-101　to　extract　deeper　features　to　enhance　classification　accuracy　and　effectively　mitigate　the　gradient　vanishing　problem　caused　by　increasing　the　depth　of　convolutional　neural　networks.　The　Depthwise　Separable-Atrous　Spatial　Pyramid　Pooling　(DS-ASPP)　with　different　dilation　rates　is　constructed　to　address　insufficient　receptive　fields,　resolving　the　issue　of　discontinuous　holes　when　extracting　large　targets.　The　Dual　Attention　Mechanism　Module　(DAMM)　is　employed　to　better　preserve　spatial　details,　enriching　feature　extraction.　In　the　decoder,　Multi-Scale　Feature　Fusion　(MS-FF)　is　utilized　to　capture　contextual　information,　integrating　shallow　and　intermediate　features　of　the　backbone　network,　thereby　enhancing　extraction　capabilities　in　complex　scenes.　MS-FF-AENet　is　employed　to　extract　and　analyze　changes　in　building　areas　at　different　time　periods,　calculating　the　engineering　waste　from　new　constructions　and　demolition　waste　from　demolished　buildings,　thereby　obtaining　the　annual　production　of　urban　construction　waste.　Furthermore,　MS-FF-AENet　is　utilized　to　extract　construction　waste　disposal　sites　at　different　time　periods,　estimating　the　amount　of　construction　waste　landfill　based　on　changes　in　landfill　waste,　indirectly　assessing　the　resource　conversion　rate　of　urban　construction　waste.　Based　on　HRSIs　of　Changping　District,　Beijing　from　2019　to　2020,　experimental　results　demonstrate:　(1)　Among　a　series　of　baseline　models　including　UNet,　SegNet,　PSPNet,　DeepLabV3+,　DSAT-Net、ConvLSR-Net　and　SDSC-UNet,　MS-FF-AENet　exhibits　advantages　in　terms　of　precision　and　efficiency　in　extracting　buildings　and　construction　waste;　(2)　During　the　period　from　2019　to　2020,　the　annual　production　of　construction　waste　in　the　study　area　due　to　urban　renewal　is　approximately　4　101　156.5　tons,　with　approximately　2　251　855.872　tons　being　landfill　waste　and　approximately　1　849,　300.628　tons　being　resource　conversion　waste,　resulting　in　a　construction　waste　resource　conversion　rate　of　45.09%,　further　corroborating　government　statistical　reports.　This　paper　provides　a　convenient　and　effective　analysis　approach　for　accurate　measurement　of　the　cost　of　urban　renewal.　©　2024　China　Ship　Scientific　Research　Center.　All　rights　reserved.