Remote　sensing　technologies,　such　as　aerial　photography　and　satellite　remote　sensing,　play　a　crucial　role　in　assessing　and　monitoring　the　changes　in　land　use　and　land　cover　(LULC)　for　large　areas.　Deep　learning　methods　are　recently　becoming　popular　for　LULC　classification　and　have　maintained　promising　performance　in　many　applications.　However,　challenges　remain　in　LULC　classification　for　the　complex　semantic　appearance　in　high-resolution　remote　sensing　images.　In　this　paper,　we　proposed　a　novel　framework　that　cascades　two　deep　learning　models　i.e.,　ResNet　and　SegNet　for　land　use　and　land　cover　classification.　The　advantages　of　strong　semantic　feature　extraction　(ResNet)　and　efficient　boundary　delineation　(SegNet)　capabilities　of　these　two　models　can　be　combined　to　derive　highly　accurate　land　use　and　land　cover　classification　results.　Our　method　performed　well　on　the　recently　developed　multisource　Dense-Pixel　Annotation　Dataset　Globe230K.