Construction　waste　is　an　inevitable　byproduct　of　urban　renewal,　causing　severe　pressure　on　the　environment,　health,　and　ecology.　Accurately　estimating　the　production　of　construction　waste　is　crucial　for　assessing　the　consumption　of　urban　renewal.　However,　traditional　manual　estimation　methods　rely　heavily　on　statistical　data　and　historical　experience,　which　lack　flexibility　in　practical　applications　and　are　time-consuming　and　labor-intensive.　In　addition,　their　accuracy　and　timeliness　need　to　be　improved　urgently.　Fortunately,　with　the　advantages　of　high-resolution　remote　sensing　images　(HRSIs)　such　as　strong　timeliness,　large　amounts　of　information,　and　macroscopic　observations,　they　are　suitable　for　the　large-scale　dynamic　change　detection　of　construction　waste.　However,　the　existing　deep　learning　models　have　a　relatively　poor　ability　to　extract　and　fuse　features　for　small　and　multi-scale　targets,　and　it　is　difficult　to　deal　with　irregularly　shaped　and　fragmented　detection　areas.　Therefore,　this　study　proposes　a　Multi-scale　Target　Attention-Enhanced　Network　(MT-AENet),　which　is　used　to　dynamically　track　and　detect　changes　in　buildings　and　construction　waste　disposal　sites　through　HRSIs　and　accurately　estimate　the　annual　production　of　urban　construction　waste.　The　MT-AENet　introduces　a　novel　encoder-decoder　architecture.　In　the　encoder,　ResNet-101　is　utilized　to　extract　high-level　semantic　features.　A　depthwise　separable-atrous　spatial　pyramid　pooling　(DS-ASPP)　module　with　different　dilation　rates　is　constructed　to　address　insufficient　receptive　fields,　resolving　the　issue　of　discontinuous　holes　when　extracting　large　targets.　A　dual-attention　mechanism　module　(DAMM)　is　employed　to　better　preserve　positional　and　channel　details.　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.　The　MT-AENet　is　used　to　extract　buildings　and　construction　waste　at　different　periods　in　the　study　area,　and　the　actual　production　and　landfill　volume　of　construction　waste　are　calculated　based　on　area　changes,　indirectly　measuring　the　rate　of　urban　construction　waste　resource　conversion.　The　experimental　results　in　Changping　District,　Beijing　demonstrate　that　the　MT-AENet　outperforms　existing　baseline　networks　in　extracting　buildings　and　construction　waste.　The　results　of　this　study　are　validated　according　to　government　statistical　standards,　providing　a　promising　direction　for　efficiently　analyzing　the　consumption　of　urban　renewal.