Construction　waste　is　unavoidable　in　the　process　of　urban　development,　causing　serious　environmental　pollution.　Accurate　assessment　of　municipal　construction　waste　generation　requires　building　construction　waste　identification　models　using　deep　learning　technology.　However,　this　process　requires　high-quality　public　datasets　for　model　training　and　validation.　This　study　utilizes　Google　Earth　and　GF-2　images　as　the　data　source　to　construct　a　specific　dataset　of　construction　waste　landfills　in　the　Changping　and　Daxing　districts　of　Beijing,　China.　This　dataset　contains　3,653　samples　of　the　original　image　areas　and　provides　mask-labeled　images　in　the　semantic　segmentation　domains.　Each　pixel　within　a　construction　waste　landfill　is　classified　into　4　categories　of　the　image　areas,　including　background　area,　vacant　landfillable　area,　engineering　facility　area,　and　waste　dumping　area.　The　dataset　contains　237,115,531　pixels　of　construction　waste　and　49,724,513　pixels　of　engineering　facilities.　The　pixel-level　semantic　segmentation　labels　are　provided　to　quantify　the　construction　waste　yield,　which　can　serve　as　the　basic　data　for　construction　waste　extraction　and　yield　estimation　both　for　academic　and　industrial　research.