High　consequence　areas　within　oilfields　are　critical　zones　for　the　safety　management　of　petroleum　transport　pipelines.　Accurately　and　efficiently　capturing　the　spatial　distribution　of　key　features　in　high-consequence　areas　of　oilfields　is　essential　for　the　smooth　operation　of　petroleum　safety　production　and　the　scientific　management　of　oilfield　regions.　However,　there　are　still　challenges　in　extraction　tasks　of　the　high-consequence　areas　of　oilfields,　such　as　diverse　ground　object　shapes,　small　spectral　differences,　and　complex　types,　and　the　extraction　results　often　include　misclassification,　omissions,　and　road　discontinuities.　To　address　these　challenges,　we　propose　an　SML_ResUnet　model　for　land　cover　extraction　in　high-consequence　areas　of　oil　fields　based　on　ResUnet　architecture.　This　model　integrates　Strip　Pooling　(SP)　units　in　the　pooling　stages　and　incorporates　Mixed　Pooling　Modules　(MPM)　and　Label　Attention　Modules　(LAM)　between　the　encoding　and　decoding　processes.　The　SP　units　are　designed　to　capture　elongated　and　isolated　features,　excluding　information　from　other　irrelevant　areas,　while　the　MPM　combines　the　advantages　of　standard　pooling　and　strip　pooling,　effectively　preserving　feature　information　across　different　spatial　positions.　The　label　attention　module　introduces　label　information　to　optimize　the　attention　probability　maps　generated　within　the　attention　module,　further　enhancing　the　extraction　results.　We　applied　the　proposed　model　on　a　high-resolution　dataset　of　a　high-consequence　area　of　an　oilfield.　The　results　of　the　ablation　experiments　indicated　that　the　proposed　SML_ResUnet　network　achieved　the　optimal　extraction　results.　The　metrics　of　Overall　Accuracy　(OA),　Mean　Intersection　over　Union　(MIoU),　and　F1-score　reached　97.24%,　84.23%,　and　91.26%,　respectively.　Compared　to　the　classical　ResUnet　model,　improvements　are　observed　in　all　evaluation　metrics　of　the　proposed　model,　with　OA,　MIoU,　and　F1-score　increasing　by　0.48%,　2.49%,　and　1.55%,　respectively.　For　a　land　cover　extraction　task　within　a　high　consequence　area　of　an　oilfield　in　Shandong　Province,　the　OA　of　the　extraction　results　averaged　at　97.66%.　We　then　extended　the　model　in　other　high-　consequence　areas　of　oilfields　in　Shandong　Province　and　achieved　an　Overall　Accuracy　(OA)　of　95.63%.　Our　results　meet　the　accuracy　requirements　for　rapid　acquisition　of　surface　information　in　large-　scale　high-　consequence　areas　of　oilfields　and　demonstrate　that　the　SML_ResUnet　model　is　particularly　suitable　for　large-scale　land　cover　extraction　tasks　within　oilfields　characterized　by　diverse　and　complex　terrain　types.　©　2024　Science　Press.　All　rights　reserved.