Home Journal of Xinjiang University(Natural Science Edition in Chinese and English) Article
PDF (497 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Abstract
Keywords
References
Show full outline
Hide outline

Applications and Reflections on Artificial Intelligence in Geography

Jianli DING1,2,3(), Xiangyu GE1,2,3, Jinjie WANG1,2,3, Shuang ZHAO1,2,3, Yue DING1,2,3, Shaofeng QIN1,2,3, Chuanmei ZHU1,2,3, Wen MA1,2,3
School of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi Xinjiang 800017, China
Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi Xinjiang 830017, China
Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi Xinjiang 830017, China
Show Author Information

Abstract

The application of artificial intelligence (AI) technology in geography is promising and widely involved in the observation, analysis, simulation and prediction of geographic processes. We take “intelligent sensing-smart expression” as a channel to sort out the manifestation of AI in geography and the current application status in various fields of geography. On this basis, the challenges of current applications in terms of intelligent processing of geographic big data, scale effects, and uncertainty of models are summarized, and future development in terms of coordination and collaboration of multi-source data, integration of models, interpretability of AI, and construction of geographic big models are proposed. It is emphasized that for AI geography applications will gradually learn a large amount of geographic element data through collaborative mining of geographic big data, enhance the integration and interpretation of models, and train big models with the ability to understand the three laws of geography.

Keywords

artificial intelligencegeographic big dataremote sensingscale effectscoordination and collaboration of multi-source data
CLC number: TP7;TP18 Document code: A Article ID: 2096-7675(2023)04-0385-013

References

[2]
XU Y J, LIU X, CAO X, et al. Artificial intelligence: a powerful paradigm for scientific research[J]. The Innovation, 2021, 2(4): 100179.
Crossref Google Scholar
[5]
JANOWICZ K, GAO S, MCKENZIE G, et al. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond[J]. International Journal of Geographical Information Science, 2020, 34(4): 625-636.
Crossref Google Scholar
[8]
PEI T, XU J, LIU Y, et al. GIScience and remote sensing in natural resource and environmental research: status quo and future perspectives[J]. Geography and Sustainability, 2021, 2(3): 207-215.
Crossref Google Scholar
[9]
LI W Y, CHEN K Y, CHEN H, et al. Geographical knowledge-driven representation learning for remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-16.
Crossref Google Scholar
[10]
TURKOGLU M O, D'ARONCO S, PERICH G, et al. Crop mapping from image time series: deep learning with multi-scale label hierarchies[J]. Remote Sensing of Environment, 2021, 264: 112603.
Crossref Google Scholar
[11]
WEISS M, JACOB F, DUVEILLER G. Remote sensing for agricultural applications: a meta-review[J]. Remote Sensing of Environment, 2020, 236: 111402.
Crossref Google Scholar
[12]
YUAN Q Q, SHEN H F, LI T W, et al. Deep learning in environmental remote sensing: achievements and challenges[J]. Remote Sensing of Environment, 2020, 241: 111716.
Crossref Google Scholar
[13]
PRADO O L, JOSÉ M J, PAULA M R N, et al. A review on deep learning in UAV remote sensing[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102456.
Crossref Google Scholar
[14]
ALVAREZ V E, CORPETTI T, HOUET T. UAV & satellite synergies for optical remote sensing applications: a literature review[J]. Science of Remote Sensing, 2021, 3: 100019.
Crossref Google Scholar
[15]
AMANI M, GHORBANIAN A, AHMADI S A, et al. Google earth engine cloud computing platform for remote sensing big data applications: a comprehensive review[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 5326-5350.
Crossref Google Scholar
[17]
JUNG J H, MAEDA M, CHANG A J, et al. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems[J]. Current Opinion in Biotechnology, 2021, 70: 15-22.
Crossref Google Scholar
[18]
ZHANG Z P, DING J L, ZHU C M, et al. Bivariate empirical mode decomposition of the spatial variation in the soil organic matter content: a case study from NW China[J]. Catena, 2021, 206: 105572.
Crossref Google Scholar
[19]
LYU G N, BATTY M, STROBL J, et al. Reflections and speculations on the progress in geographic information systems (GIS): a geographic perspective[J]. International Journal of Geographical Information Science, 2019, 33(2): 346-367.
Crossref Google Scholar
[20]
SINGLETON A, ARRIBAS B D. Geographic data science[J]. Geographical Analysis, 2021, 53(1): 61-75.
Crossref Google Scholar
[21]
HOMER C, DEWITZ J, JIN S M, et al. Conterminous United States land cover change patterns 2001—2016 from the 2016 national land cover database[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162: 184-199.
Crossref Google Scholar
[22]
FRANKLIN S E. Interpretation and use of geomorphometry in remote sensing: a guide and review of integrated applications[J]. International Journal of Remote Sensing, 2020, 41(19): 7700-7733.
Crossref Google Scholar
[25]
XU Z C, CHAU S N, CHEN X Z, et al. Assessing progress towards sustainable development over space and time[J]. Nature, 2020, 577(7788): 74-78.
Crossref Google Scholar
[26]
GE X Y, DING J L, TENG D X, et al. Updated soil salinity with fine spatial resolution and high accuracy: the synergy of Sentinel-2 MSI, environmental covariates and hybrid machine learning approaches[J]. Catena, 2022, 212: 106054.
Crossref Google Scholar
[28]
WU S S, WANG Z Y, DU Z H, et al. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships[J]. International Journal of Geographical Information Science, 2021, 35(3): 582-608.
Crossref Google Scholar
[29]
SHEYKHMOUSA M, MAHDIANPARI M, GHANBARI H, et al. Support vector machine versus random forest for remote sensing image classification: a meta-analysis and systematic review[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 6308-6325.
Crossref Google Scholar
[30]
HONG D F, GAO L R, YOKOYA N, et al. More diverse means better: multimodal deep learning meets remote-sensing imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(5): 4340-4354.
Crossref Google Scholar
[31]
MAJUMDAR S, SMITH R, BUTLER J J, et al. Groundwater withdrawal prediction using integrated multitemporal remote sensing data sets and machine learning[J]. Water Resources Research, 2020, 56(11): 1-25.
Crossref Google Scholar
[32]
CAO R, TU W, YANG C X, et al. Deep learning-based remote and social sensing data fusion for urban region function recognition[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 82-97.
Crossref Google Scholar
[33]
ZHONG L H, HU L N, ZHOU H. Deep learning based multi-temporal crop classification[J]. Remote Sensing of Environment, 2019, 221: 430-443.
Crossref Google Scholar
[34]
SONG J, GAO S H, ZHU Y Q, et al. A survey of remote sensing image classification based on CNNs[J]. Big Earth Data, 2019, 3(3): 232-254.
Crossref Google Scholar
[35]
TAHMASEBI P, KAMRAVA S, BAI T, et al. Machine learning in geo- and environmental sciences: from small to large scale[J]. Advances in Water Resources, 2020, 142: 103619.
Crossref Google Scholar
[36]
VARGAS M J E, SRIVASTAVA S, TUIA D, et al. OpenStreetMap: challenges and opportunities in machine learning and remote sensing[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 9(1): 184-199.
Crossref Google Scholar
[38]
MA L, LIU Y, ZHANG X L, et al. Deep learning in remote sensing applications: a meta-analysis and review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152: 166-177.
Crossref Google Scholar
[39]
CARTER C, LIANG S L. Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 78: 86-92.
Crossref Google Scholar
[40]
ZHANG E Z, LIU L, HUANG L C, et al. An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery[J]. Remote Sensing of Environment, 2021, 254: 112265.
Crossref Google Scholar
[41]
SU H J, DU Q, CHEN G S, et al. Optimized hyperspectral band selection using particle swarm optimization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2659-2670.
Crossref Google Scholar
[42]
CHEN Y P, WANG S D, HAN W H, et al. A new air pollution source identification method based on remotely sensed aerosol and improved glowworm swarm optimization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3454-3464.
Crossref Google Scholar
[43]
LIAO J F, TANG L N, SHAO G F, et al. A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence[J]. International Journal of Geographical Information Science, 2014, 28(4): 720-738.
Crossref Google Scholar
[46]
WANG J J, DING J L, GE X Y, et al. Assessment of ecological quality in Northwest China (2000—2020) using the google earth engine platform: climate factors and land use/land cover contribute to ecological quality[J]. Journal of Arid Land, 2022, 14: 1196-1211.
Crossref Google Scholar
[47]
XU K, MA X G, LIU G, et al. Geological hazards susceptibility evaluation based on GA-BPNN: a case study of Xingye County[J]. Earth and Space Science, 2022, 9(12): e2019EA000929.
Crossref Google Scholar
[48]
JAMALI A. Improving land use land cover mapping of a neural network with three optimizers of multi-verse optimizer, genetic algorithm, and derivative-free function[J]. The Egyptian Journal of Remote Sensing and Space Science, 2021, 24(3): 373-390.
Crossref Google Scholar
[49]
LI J Z, AI T H, LIU P C, et al. Continuous scale transformations of linear features using simulated annealing-based morphing[J]. ISPRS International Journal of Geo-Information, 2017, 6(8): 242.
Crossref Google Scholar
[50]
LAVALLIN A, DOWNS J A. Machine learning in geography–past, present, and future[J]. Geography Compass, 2021, 15(5): e12563.
Crossref Google Scholar
[51]
LI H F, LI Y, ZHANG G, et al. Global and local contrastive self-supervised learning for semantic segmentation of HR remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.
Crossref Google Scholar
[52]
WU T J, LUO J C, DONG W, et al. Geo-object-based soil organic matter mapping using machine learning algorithms with multi-source geo-spatial data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(4): 1091-1106.
Crossref Google Scholar
[53]
CHANG Z L, DU Z, ZHANG F, et al. Landslide susceptibility prediction based on remote sensing images and GIS: comparisons of supervised and unsupervised machine learning models[J]. Remote Sensing, 2020, 12(3): 502.
Crossref Google Scholar
[54]
STEVENSON M, MUES C, BRAVO C. Deep residential representations: using unsupervised learning to unlock elevation data for geo-demographic prediction[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 187: 378-392.
Crossref Google Scholar
[55]
YAN B, JANOWICZ K, MAI G C, et al. A spatially explicit reinforcement learning model for geographic knowledge graph summarization[J]. Transactions in GIS, 2019, 23(3): 620-640.
Crossref Google Scholar
[57]
RAHMATI O, CHOUBIN B, FATHABADI A, et al. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods[J]. Science of the Total Environment, 2019, 688: 855-866.
Crossref Google Scholar
[58]
MAXWELL A E, WARNER T A, FANG F. Implementation of machine-learning classification in remote sensing: an applied review[J]. International Journal of Remote Sensing, 2018, 39(9): 2784-2817.
Crossref Google Scholar
[59]
LI Y S, LI X W, ZHANG Y J, et al. Cost-efficient information extraction from massive remote sensing data: when weakly supervised deep learning meets remote sensing big data[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 120: 103345.
Crossref Google Scholar
[60]
TUIA D, VOLPI M, COPA L, et al. A survey of active learning algorithms for supervised remote sensing image classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3): 606-617.
Crossref Google Scholar
[61]
ZHANG L P, ZHANG L F, DU B. Deep learning for remote sensing data: a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2): 22-40.
Crossref Google Scholar
[62]
WANG R M, DING J L, GE X Y, et al. Impacts of climate change on the wetlands in the arid region of Northwestern China over the past 2 decades[J]. Ecological Indicators, 2023, 149: 110168.
Crossref Google Scholar
[63]
NIAN R, LIU J F, HUANG B. A review on reinforcement learning: introduction and applications in industrial process control[J]. Computers & Chemical Engineering, 2020, 139: 106886.
Crossref Google Scholar
[64]
FENG J, LI D, GU J, et al. Deep reinforcement learning for semisupervised hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-19.
Crossref Google Scholar
[65]
ZHANG C, SARGENT I, PAN X, et al. Joint deep learning for land cover and land use classification[J]. Remote Sensing of Environment, 2019, 221: 173-187.
Crossref Google Scholar
[66]
ZHU X X, TUIA D, MOU L C, et al. Deep learning in remote sensing: a comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4): 8-36.
Crossref Google Scholar
[67]
KUSSUL N, LAVRENIUK M, SKAKUN S, et al. Deep learning classification of land cover and crop types using remote sensing data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(5): 778-782.
Crossref Google Scholar
[68]
KATTENBORN T, LEITLOFF J, SCHIEFER F, et al. Review on convolutional neural networks (CNN) in vegetation remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 173: 24-49.
Crossref Google Scholar
[69]
MA Z F, ZHANG H, LIU J. MM-RNN: a multimodal RNN for precipitation nowcasting[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4101914.
Crossref Google Scholar
[70]
LYU H, LU H, MOU L C, et al. Long-term annual mapping of four cities on different continents by applying a deep information learning method to landsat data[J]. Remote Sensing, 2018, 10(3): 471.
Crossref Google Scholar
[71]
TIAN H R, WANG P X, TANSEY K, et al. An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China[J]. Agricultural and Forest Meteorology, 2021, 310: 108629.
Crossref Google Scholar
[72]
PARK S, IM J, HAN D, et al. Short-term forecasting of satellite-based drought indices using their temporal patterns and numerical model output[J]. Remote Sensing, 2020, 12(21): 3499.
Crossref Google Scholar
[74]
DENG P F, XU K J, HUANG H. When CNNs meet vision transformer: a joint framework for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
Crossref Google Scholar
[75]
GREKOUSIS G. Artificial neural networks and deep learning in urban geography: a systematic review and meta-analysis[J]. Computers, Environment and Urban Systems, 2019, 74: 244-256.
Crossref Google Scholar
[76]
PADARIAN J, MINASNY B, MCBRATNEY A B. Machine learning and soil sciences: a review aided by machine learning tools[J]. Soil, 2020, 6(1): 35-52.
Crossref Google Scholar
[77]
WADOUX A M J C, MINASNY B, MCBRATNEY A B. Machine learning for digital soil mapping: applications, challenges and suggested solutions[J]. Earth-Science Reviews, 2020, 210: 103359.
Crossref Google Scholar
[78]
GAUTAM K, SHARMA P, DWIVEDI S, et al. A review on control and abatement of soil pollution by heavy metals: emphasis on artificial intelligence in recovery of contaminated soil[J]. Environmental Research, 2023, 225: 115592.
Crossref Google Scholar
[80]
BESALATPOUR A, HAJABBASI M A, AYOUBI A, et al. Prediction of soil physical properties by optimized support vector machines[J]. International Agrophysics, 2012, 26(2): 109-115.
Crossref Google Scholar
[81]
AITKENHEAD M J, COULL M C, TOWERS W, et al. Predicting soil chemical composition and other soil parameters from field observations using a neural network[J]. Computers and Electronics in Agriculture, 2012, 82: 108-116.
Crossref Google Scholar
[82]
GE X Y, DING J L, JIN X L, et al. Estimating agricultural soil moisture content through UAV-based hyperspectral images in the arid region[J]. Remote Sensing, 2021, 13(8): 1562.
Crossref Google Scholar
[83]
RIVERA J I, BONILLA C A. Predicting soil aggregate stability using readily available soil properties and machine learning techniques[J]. Catena, 2020, 187: 104408.
Crossref Google Scholar
[84]
AZIZI A, GILANDEH Y A, MESRI G T, et al. Classification of soil aggregates: a novel approach based on deep learning[J]. Soil and Tillage Research, 2020, 199: 104586.
Crossref Google Scholar
[85]
PADARIAN J, MINASNY B, MCBRATNEY A B. Using deep learning for digital soil mapping[J]. Soil, 2019, 5(1): 79-89.
Crossref Google Scholar
[88]
CHEW A W Z, HE R F, ZHANG L M. Multiscale homogenized predictive modelling of flooding surface in urban cities using physics-induced deep AI with UPC[J]. Journal of Cleaner Production, 2022, 363: 132455.
Crossref Google Scholar
[89]
FOTOVATIKHAH F, HERRERA M, SHAMSHIRBAND S, et al. Survey of computational intelligence as basis to big flood management: challenges, research directions and future work[J]. Engineering Applications of Computational Fluid Mechanics, 2018, 12(1): 411-437.
Crossref Google Scholar
[90]
MOSAVI A, OZTURK P, CHAU K W. Flood prediction using machine learning models: literature review[J]. Water, 2018, 10(11): 1536.
Crossref Google Scholar
[91]
ZHANG Y, HAN W T, ZHANG H H, et al. Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms[J]. Journal of Hydrology, 2023, 617: 129086.
Crossref Google Scholar
[93]
NOURANI V, ELKIRAN G, ABDULLAHI J. Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements[J]. Journal of Hydrology, 2019, 577: 123958.
Crossref Google Scholar
[95]
KHOSRAVI K, GOLKARIAN A, OMIDVAR E, et al. Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches[J]. Acta Geophysica, 2023, 71(2): 1015-1031.
Crossref Google Scholar
[96]
DONAT M G, ANGÉLIL O, UKKOLA A M. Intensification of precipitation extremes in the world's humid and water-limited regions[J]. Environmental Research Letters, 2019, 14(6): 065003.
Crossref Google Scholar
[97]
NOURANI V, KISI Ö, KOMASI M. Two hybrid artificial intelligence approaches for modeling rainfall-runoff process[J]. Journal of Hydrology, 2011, 402(1/2): 41-59.
Crossref Google Scholar
[98]
POURSAEID M, POURSAEID A H, SHABANLOU S. A comparative study of artificial intelligence models and a statistical method for groundwater level prediction[J]. Water Resources Management, 2022, 36(5): 1499-1519.
Crossref Google Scholar
[101]
LI D W, SHI G L, LI J S, et al. PlantNet: a dual-function point cloud segmentation network for multiple plant species[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 184: 243-263.
Crossref Google Scholar
[102]
RYO M, ANGELOV B, MAMMOLA S, et al. Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models[J]. Ecography, 2021, 44(2): 199-205.
Crossref Google Scholar
[103]
ZHANG P, GUO Z L, ULLAH S, et al. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture[J]. Nature Plants, 2021, 7(7): 864-876.
Crossref Google Scholar
[104]
SHAIKH T A, RASOOL T, LONE F R. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming[J]. Computers and Electronics in Agriculture, 2022, 198: 107119.
Crossref Google Scholar
[105]
SHARMA A, JAIN A, GUPTA P, et al. Machine learning applications for precision agriculture: a comprehensive review[J]. IEEE Access, 2020, 9: 4843-4873.
Crossref Google Scholar
[107]
ORCHI H, SADIK M, KHALDOUN M. On using artificial intelligence and the internet of things for crop disease detection: a contemporary survey[J]. Agriculture-Basel, 2022, 12(1): 9.
Crossref Google Scholar
[108]
MURUGANANTHAM P, WIBOWO S, GRANDHI S, et al. A systematic literature review on crop yield prediction with deep learning and remote sensing[J]. Remote Sensing, 2022, 14(9): 1990.
Crossref Google Scholar
[111]
HUNTINGFORD C, JEFFERS E S, BONSALL M B, et al. Machine learning and artificial intelligence to aid climate change research and preparedness[J]. Environmental Research Letters, 2019, 14(12): 124007.
Crossref Google Scholar
[112]
DEWITTE S, CORNELIS J P, MÜLLER R, et al. Artificial intelligence revolutionises weather forecast, climate monitoring and decadal prediction[J]. Remote Sensing, 2021, 13(16): 3209.
Crossref Google Scholar
[113]
MEYDANI A, DEHGHANIPOUR A, SCHOUPS G, et al. Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: application to Urmia Lake Basin, Iran[J]. Journal of Hydrology-Regional Studies, 2022, 44: 101228.
Crossref Google Scholar
[114]
ZHANG R, CHEN Z Y, XU L J, et al. Meteorological drought forecasting based on a statistical model with machine learning techniques in Shaanxi province, China[J]. Science of the Total Environment, 2019, 665: 338-346.
Crossref Google Scholar
[115]
AMUTHADEVI C, VIJAYAN D S, RAMACHANDRAN V. Development of air quality monitoring (AQM) models using different machine learning approaches[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 13(1): 1-13.
Crossref Google Scholar
[116]
JIN X Y, DING J L, GE X Y, et al. Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions[J]. PeerJ, 2022, 10: e13203.
Crossref Google Scholar
[117]
ZHANG G Q, YAO T D, XIE H J, et al. Response of Tibetan Plateau lakes to climate change: trends, patterns, and mechanisms[J]. Earth-Science Reviews, 2020, 208: 103269.
Crossref Google Scholar
[118]
DEGROOT D, ANCHUKAITIS K, BAUCH M, et al. Towards a rigorous understanding of societal responses to climate change[J]. Nature, 2021, 591(7851): 539-550.
Crossref Google Scholar
[119]
ALI M Z, CHU H J, CHEN Y C, et al. Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification[J]. Environmental Earth Sciences, 2021, 80(6): 1-21.
Crossref Google Scholar
[120]
YU D L, FANG C L. Urban remote sensing with spatial big data: a review and renewed perspective of urban studies in recent decades[J]. Remote Sensing, 2023, 15(5): 1307.
Crossref Google Scholar
[121]
DEY N N, AL R A, KAFY A A, et al. Geospatial modelling of changes in land use/land cover dynamics using multi-layer perceptron Markov chain model in Rajshahi City, Bangladesh[J]. Environmental Challenges, 2021, 4: 100148.
Crossref Google Scholar
[122]
YANG L P, DRISCOL J S, SARIGAI S, et al. Google earth engine and artificial intelligence (AI): a comprehensive review[J]. Remote Sensing, 2022, 14(14): 3253.
Crossref Google Scholar
[123]
SHI W Z, ZHANG M, ZHANG R, et al. Change detection based on artificial intelligence: state-of-the-art and challenges[J]. Remote Sensing, 2020, 12(10): 1688.
Crossref Google Scholar
[124]
ZHANG X, HAN L X, HAN L H, et al. How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery?[J]. Remote Sensing, 2020, 12(3): 417.
Crossref Google Scholar
[125]
IENCO D, INTERDONATO R, GAETANO R, et al. Combining sentinel-1 and sentinel-2 satellite image time series for land cover mapping via a multi-source deep learning architecture[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158: 11-22.
Crossref Google Scholar
[126]
LI W J, DONG R M, FU H H, et al. Integrating google earth imagery with landsat data to improve 30-m resolution land cover mapping[J]. Remote Sensing of Environment, 2020, 237: 111563.
Crossref Google Scholar
[129]
LI Y, YANG J C. Few-shot cotton pest recognition and terminal realization[J]. Computers and Electronics in Agriculture, 2020, 169: 105240.
Crossref Google Scholar
[131]
GAO C D, GUO Q Q, JIANG D, et al. Theoretical basis and technical methods of cyberspace geography[J]. Journal of Geographical Sciences, 2019, 29(12): 1949-1964.
Crossref Google Scholar
[136]
QIN C Z, AN Y M, LIANG P, et al. Soil property mapping by combining spatial distance information into the soil land inference model (SoLIM)[J]. Pedosphere, 2021, 31(4): 638-644.
Crossref Google Scholar
[137]
GE X Y, DING J L, TENG D X, et al. Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 10296.
Crossref Google Scholar
[138]
MENDES W D S, NETO L G M, DEMATTÊ J A M, et al. Is it possible to map subsurface soil attributes by satellite spectral transfer models?[J]. Geoderma, 2019, 343: 269-279.
Crossref Google Scholar
[139]
KHALEDIAN Y, MILLER B A. Selecting appropriate machine learning methods for digital soil mapping[J]. Applied Mathematical Modelling, 2020, 81: 401-418.
Crossref Google Scholar
[140]
KWAN M P. Algorithmic geographies: big data, algorithmic uncertainty, and the production of geographic knowledge[J]. Annals of the American Association of Geographers, 2016, 106(2): 274-282.
Google Scholar
[141]
CRAMPTON J W, GRAHAM M, POORTHUIS A, et al. Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb[J]. Cartography and Geographic Information Science, 2013, 40(2): 130-139.
Crossref Google Scholar
[142]
LIU X T, CHEN M, CLARAMUNT C, et al. Geographic information science in the era of geospatial big data: a cyberspace perspective[J]. The Innovation, 2022, 3(5): 100279.
Crossref Google Scholar
[144]
REICHSTEIN M, CAMPS V G, STEVENS B, et al. Deep learning and process understanding for data-driven earth system science[J]. Nature, 2019, 566(7743): 195-204.
Crossref Google Scholar
[146]
JIANG S J, ZHENG Y, SOLOMATINE D. Improving AI system awareness of geoscience knowledge: symbiotic integration of physical approaches and deep learning[J]. Geophysical Research Letters, 2020, 47(13): e2020GL088229.
Crossref Google Scholar
[147]
AVAND M, MOHAMMADI M, MIRCHOOLI F, et al. A new approach for smart soil erosion modeling: integration of empirical and machine-learning models[J]. Environmental Modeling & Assessment, 2023, 28(1): 145-160.
Crossref Google Scholar
[148]
LIU P Y, BILJECKI F. A review of spatially-explicit GeoAI applications in urban geography[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102936.
Crossref Google Scholar
[151]
LI W W, HSU C Y, HU M S. Tobler's first law in GeoAI: a spatially explicit deep learning model for terrain feature detection under weak supervision[J]. Annals of the American Association of Geographers, 2021, 111(7): 1887-1905.
Crossref Google Scholar
Journal of Xinjiang University(Natural Science Edition in Chinese and English)
Volume 40 Issue 4,
July 2023
Pages 385-397
DOI: 10.13568/j.cnki.651094.651316.2023.06.14.0001
Cite this article:
DING J, GE X, WANG J, et al. Applications and Reflections on Artificial Intelligence in Geography. Journal of Xinjiang University(Natural Science Edition in Chinese and English), 2023, 40(4): 385-397. https://doi.org/10.13568/j.cnki.651094.651316.2023.06.14.0001
Metrics & Citations  
Article History
Copyright
Return