An Automated Approach for Constructing Road Network
Graph from Multispectral Images
Weihua Sun a and David W.Messinger a
a Chester F.Carlson Center for Imaging Science,Rochester Institute of Technology,Rochester,
NY,USA
ABSTRACT
We present an approach for automatically building a road network graph from multispectral WorldView II images in suburban and urban areas.In this graph,the road parts are represented by edges and their connectivity by vertices.This approach consists of an image processing chain utilizing both high-resolution spatial features as well as multiple band spectral signatures from satellite images.Based on an edge-preserving?ltered image,a two-pass spatial-spectral?ood?ll technique is adopted to extract a road class map.This technique requires only one pixel as the initial training set and collects spatially adjacent and spectrally similar pixels to the initial points as a second level training set for a higher accuracy asphalt classi?cation.Based on the road class map,a road network graph is built after going through a curvilinear detector and a knowledge based system.The graph projects a logical representation of the road network in an urban image.Rules can be made to?lter salient road parts with di?erent width as well as ruling out parking lots from the asphalt class map.This spatial spectral joint approach we propose here is capable of building up a road network connectivity graph and this graph lays
a foundation for further road related tasks.
1.INTRODUCTION
Road network extraction from satellite images has become an active and important research area in recent decades.The extracted road network can be used for several applications such as providing road layout for constructing geographic information systems(GIS)or corrections for existing GIS database.1–3The road net-work can also be used for image registration,4change detection5as well as vehicle detection.6The availability of accurate high resolution multispectral images delivered by the new generation of sensors has provided the potential to discriminate very subtle details in urban scenes.The WorldView-2Satellite delivers8-band images covering the visible and near infrared(400-1040nm)wavelengths with a2.0meter ground spatial resolution. These images provide us rich spatial and spectral information and can be utilized for road network extraction.
The study of road detection can be dated back three decades ago and a number of approaches have been proposed to tackle the problem.Mena3surveyed the techniques for automated road extraction and extensively reviewed the related approaches.While a number of techniques can be used to facilitate the task of road extraction,it often requires a series of processing to export the?nal road network from the input of aerial images. With the increase in complexity of the road network,this task entails an integrated system incorporating both low-level image-based recognition techniques as well as higher-level knowledge.
Image-based recognition often involves obtaining initial road candidates based on features in images.Under this category,line-based approaches are often explored since roads are mostly linear or curvilinear structures. Baumgartner et al.7built a multi-resolution aerial image set and extracted lines from low resolution images; similarly Couloigner and Ranchin8took a multi-resolution approach using wavelet transform to?nd street strips. Jin and Davis9also employed a multi-scale approach by thresholding spectral Normalized Di?erence Vegetation Index to locate road pixels for suburban areas.Mena and Malpica10evaluated the texture statistics to produce a binary road segmentation.Shao et al.11used a fast linear detector and non maximum suppression to extract road centerlines on high contrast road pixels with increased performance.Besides linear features,road intersections are another signature in road networks.Koutaki and Uchimura12considered matching a binary road image with a group of prior shape models to identify intersections.Morphological operators are also extensively used, Ref.[10,13,14]obtained road centerlines using a skeleton operator from a segmented binary road image.Ref.9 developed a directional morphological operations to separate roads from dark or bright building and parking lots Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII,
edited by Sylvia S. Shen, Paul E. Lewis, Proc. of SPIE Vol. 8390, 83901W · ? 2012 SPIE
CCC code: 0277-786X/12/$18 · doi: 10.1117/12.918692
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Figure1:The?owchart for the road network extraction system.
in city block scenes.Recently Mnih and Hinton performed a neural network approach to detect road structure under di?erent occulsions.15
Active contour or snake model16is a frequently used method for road network extraction.Ziplock ribbon active contour derived from the snake model is used to bridge gaps on road network blocked by shadows.17Song et al.18also succeeded in road con?ation using active contours together with existing geospatial vector road data. The active contour model is known to be sensitive to initialization and thus require a high level of con?dence in initial estimation.Alternatively a grouping or tracing scheme is also possible to bridge the gaps from the initial estimates.Baumgartner et al7used a line fusion technique by iteratively relaxing the maximum length of the gap to be bridged.Hu and Tao19employed a probabilistic geometric relationship model to hierarchically group line segments.Amini et al.13also utilized collinearity and spline interpolation to group and re?ne their detection results.Hu et al.20traces the road footprint using a spoke-wheel operator from road seedings and such operator is capable of tracing both straight and highly curved roads.Contextual information is also employed for assistance in road detection.Jin and Davis9proposed using di?erent algorithms for urban and suburban scenes to take advantage of their individual characteristics.Mena14performed topological pruning of erroneous road branches using superposition of an existing GIS building layer.
In this paper,we design a systematic work?ow incorporating novel algorithms applied to the problem of road network extraction.We use multispectral WorldView2images for detection since they contain rich spectral and spatial information.The algorithm follows the?owchart illustrated in Figure1.Firstly?ltering21is applied to reduce the noise and enhance the important edges;next a spectral?ood?lling technique is used to produce a binary asphalt image.Road-like structures are then extracted using a curvilinear detector based on template matching;these structures are then processed by a knowledge-based system.The extracted road network is ?nally presented with its centerline position as well as width and orientation information and can be easily converted to a vectorized shape?le for processing with GIS packages.
The remainder of this paper is organized as follows:the spectral?ood?lling is introduced in Section2and the curvilinear detector is detailed in Section3.The experimental results on a set of scenes are shown in Section 4.Finally we present our discussion in Section5.
2.FLOOD FILLING FOR MSI
The?rst step for road network extraction is to properly determine the connected asphalt pixels in satellite images.A conventional approach is to perform a supervised classi?cation where an adequate amount of training
samples are needed to properly separate di?erent classes.These methods would often require training samples not only for the asphalt,but for vegetation,waters,concrete,soil etc.as well.In addition,classi?cation is often carried out solely in the spectral space so the spatial information is completely ignored.
The?ood?lling procedure is a widely used approach in region growing and segmentation for binary,grayscale or RGB images.22Flood?lling requires one or several seed points.It iteratively?lls the neighboring pixels by merging those possessing similar digital count values.The process resembles a?ooding pattern sprung from a speci?c spot.In this paper,the?ood?lling is extended for processing multispectral or hyperspectral images; the?lling is carried out by comparing spectral similarity.In our case,two spectral similarity metrics are used, namely the spectral angle mapper(SAM)and the spectral Euclidean distance(ED).23SAM for two spectral
vectors v1and v2is de?ned as
SAM( v1, v2)=cos?1(
v1· v2
| v1|×| v2|
)(1)
where·indicates the inner product.SAM basically computes a normalized correlation between two vectors. Spectral ED is de?ned as
ED( v1, v2)=|| v1? v2||.(2) The?lling proceeds only if both SAM and ED between the neighboring pixels are smaller than the prede?ned thresholds.This ensures the?lled areas touch the boundaries of the roads.The technique supersedes the conventional classi?cation method in that it only requires minimal training of only one or several seed points;it also takes into account the spatial continuity for road pixels.In most cases,a de-noising process is carried out to improve image quality and remove noise and clutter.In this paper,a trilateral?ltering process21is employed to?lter the spectral image.This technique is derived from the conventional bilateral?lters24and it can reduce small clutter across the image while keeping edges with strong contrast.
The?ood?ll only gives one connected area given one seed.While the road network is generally connected, it is often not the case in real images.Several seed points may be needed in order to correctly identify all these areas.Alternatively,we design a two-step approach to locate asphalt pixels in the entire image using limited seeds.The?rst step is to grow a connected area using a given seed;then we apply a Gaussian Maximum Likelihood classi?er(GML)23on the entire image using the connected area as a training set.The high-?delity pixels are used as seeds for a second-pass?lling to ensure most of the asphalt pixels are obtained.
The generic?ood?lling process performs a signi?cant amount of neighbor comparison,many of which are often repeated thus a number of optimization schemes have been proposed and well programmed in most image processing toolboxes;yet these toolboxes are mostly designed only for black-white or RGB images.However,one can still optimize the multispectral?lling process by utilizing functions in these toolboxes.This is made possible by computing a joint SAM edge and ED edge map using provided thresholds;a binary?ood?lling,which is available in most toolboxes,can then be carried out.
The optimized multispectral?ood?ll approach features a two step process,?rst a comparison of SAM and/or ED with the seed pixels is performed across the image and is quantized to binary images using prede?ned thresholds such that spectrally similar pixels are marked as1and others as0.The process is carried out for each seed pixel respectively and combined using an OR operation.This rules out all the pixels that are not similar to any of the seed pixels and the output is a similarity binary map.Next we mark spectral edge pixels on this binary map as zeros.While several approaches can achieve this,25,26an edge pixel can be simply determined by comparing itself to its neighbors.To be speci?c,SAM and/or ED is calculated with the north,west,north-west pixels respectively and is marked as an edge pixel if any of results is larger than the thresholds;pixels in other directions are not considered to avoid the unnecessary repetition.These edge pixels are marked as zeros on the binary map;then the binary?ood?ll from the seed pixels can be carried out on this image using 4-way connectivity to obtain the connected part.Results from the optimized?ood?lling process will depend on the speci?c de?nition of the multispectral edge.While the results may be somehow di?erent than the generic algorithm,they are essentially very similar and can well satisfy our need for initial road detection.
3.CUR VILINEAR STRUCTURE DETECTION
Using?ood?lling,a binary asphalt map is produced;yet urban and suburban scenes contain a number of structures that are built using asphalt other than roads.A curvilinear detection technique is often required to separate roads from other objects such as parking lots or building rooftops.In this paper,we follow a strategy similar to Ref.[27]to obtain an initial set of curvilinear pixels.A set of curvilinear detectors with di?erent width and direction form a?lter bank and each?lter in the bank is convoluted with the binary map.The detectors take the form of long rectangular shapes and are de?ned as
f(x)=
1
2wr
×
?
?
?
1|x| ?w2r w2≤|x| 0|x|≥w2+r (3) where w is the length of the short rectangle edge and is representative of the road width and l is the length of the long edge;the region between w2and w2+r is designed to capture the edge response so that the template gives maximum response when correlated with two parallel edges with asphalt pixels?lled in between separated by w. It should be noted that the non-asphalt pixels in the binary image should be mapped to-1before convolution. It is also possible to use smooth varying kernels yet we found Eq.3produces best results. It is worth mentioning that the proper choice of r is important for the detection.r is expressed as the relaxed space to correlate with road boundaries.If r is too narrow,the template may not properly capture the road boundary.While it is possible to choose a higher value,the space occupied by r may overlap with other asphalt structures such as parallel roads or parking lots on urban scenes;the overlap may decrease the matching response and produce false negatives.In our experiment,r is set to2to produce most stable responses.We have also normalized the maximum possible response for di?erent?lters so that the matching response across di?erent ?lters can be compared.This is necessary since we need to determine the most likely width and orientation for each pixel. A maximum response score map can be obtained after?ltering;the corresponding width and direction are also recorded.High response score indicates higher possibility for road structure.Non-maximum suppression and hysteresis thresholding are employed in order to obtain the centerline of the road structure.This guarantees a thin representation of the road. The detector produces adequate accuracy for simple road structures.Yet it generates high responses not only for road segments,but also for long edges of large parking lots and tight house complex forming a line. The detector produces a large amount of false alarms for scenes abundant of such interference.In addition, the detector misses a few places when the road is merged with a roadside parking lot or partially occluded by overhead trees.In these scenarios,we use a follow-up knowledge-based system to further increase the overall detection accuracy.The knowledge-based system segments the initial road network into logical parts using the geometric and contextual connectivity and applies a set of rules to eliminate the erroneous segments.The details of the knowledge based system are described in Ref.[28]. 4.EXPERIMENTAL RESULTS The proposed approach has been applied to a set of scenes.First we demonstrate the process in details using the Trona scene;then the results and analysis for other scenes are presented. 4.1The Trona Scene The Trona scene consists of a small town in Trona,California.An RGB image of the scene from the WorldView-2 sensor is shown in Figure2.The roads are generally straight but appear to have varying color.We?rst remove the noise from the image using trilateral?ltering21with a SAM threshold of0.1and an ED of100considering the WorldView-2image has a dynamic range of11bit(2048).Due to the nature of this joint spatio-spectral ?ltering,the resultant image appears to be much less noisy and the road edges stand out as shown in Figure3a. Two pixels on the road are manually selected to start?ood?lling for obtaining all the asphalt pixels.These two pixels are marked by red dots in Figure3a and the resultant binary asphalt map from the?ood?ll is shown in Figure3b. Figure2:RGB bands of the Trona Scene. (a)RGB bands of?ltered Trona Scene.(b)Asphalt map from?ood?ll. Figure3:The?ood?ll process on the Trona scene,it comprises two steps:?rst a trilateral?ltering and next a multispectral?ood?ll. (a)Response from the curvilinear detectors.Brighter val- ues indicate higher response thus more likely to be road segments.(b)The initial guess of the road centerline after non-maximum suppression and hysteresis thresholding. Figure 4:The curvilinear detection process on the Trona scene. Figure 5:Two types of false alarms produced from the curvilinear detectors.The left panel illustrates the case when a portion of the main road is obscured by the surrounding while the right panel shows a case where consecutive houses produce false alarms.In each panel,the top-left is the RGB image;the top-right is the binary asphalt map;the lower-left is the response from the curvilinear detector and the lower-right is the centerline produced by thresholding. Following the ?ood ?lling,the curvilinear detector is applied.The matching process produces higher responses (Figure 4a)when the signature of road is prominent.Then the initial road centerline can be obtained using non-maximum suppression and hysteresis thresholding as shown in Figure 4b.The lower and upper thresholds for hysteresis thresholding are 0.4and 0.6respectively.It can be seen that the curvilinear detector performs well on this scene and has extracted most of the major road pixels,but it also produces a number of false alarms on consecutive houses.The detector also misses one part on the major road where the curvilinear signature is weakened by its surroundings.These two cases are shown in Figure 5. To eradicate such false alarms,it is necessary to seek the high-level knowledge-based recognition approach.The knowledge-based system is advantageous in its ability to process segments instead of pixels.The curvilinear detector produces 7339pixels as potential centerline pixels,yet the knowledge-based system only needs to process 107segments,making the computation much more e?cient. The ?nal result (Figure 6)demonstrates that the entire system performs very well on this scene.The system not only detects almost all the road centerlines in the scene but can recover the full road structure as well using the width and orientation information from the curvilinear detector.It missed only one road on the bottom and Figure6:Knowledge-based system applied to the detection of the road network. Figure7:RGB band for RIT scene.The red dot on the top left parking lot is the seed pixel for?ood?lling. this is because the road edge is obscured and it is not well picked by the?ood?ll. 4.2The RIT Campus Scene The RIT campus scene was collected in June2009from the WorldView-2sensor.It displays the main campus of Rochester Institute of Technology and the RGB bands are shown in Figure7.The RIT campus scene is challenging in that it contains large parking lots and curved roads.It also has overhead trees blocking portions of the road.We use the GIS database from Monroe County Government as truth data for comparison.The GIS database also has some inconsistencies with the image due to recent constructions on the campus.Only one pixel is chosen for?ood?lling and it is shown by the red dot in Figure7.The results are shown in Figure8.We can see the detector successfully detects all the major roads surrounding the main campus even though they are curved.It missed a few small circles since they do not possess any curvilinear features.One interesting aspect is that the detector also identi?es the parking lanes as roads,which are not present in the truth data.However, we would argue that such detection as correct since they follow the general de?nition of road and also appears in many other GIS database such as Google Maps. In order to obtain the accuracy statistics of our detection,the GIS data is projected into the image intrinsic space,connected using linear interpolation,and dilated to be5pixels wide to avoid mis-registration.Detection is marked as a hit when it overlaps with the dilated truth data;otherwise it is marked as a false alarm.To obtain the miss rate,we carry out a similar procedure by dilating the detected road centerline and count the pixels outside the dilated region.The hit rate is computed as the fraction of the hit pixels falling within all (a)Flood?ll result(b)Extracted road network (c)Truth data(d)Extracted centerline. Figure8:Results for the RIT scene. pixels from the truth and the miss rate as the fraction of the missed pixels within all pixels from the truth;the false alarm rate is calculated as the fraction of false alarm pixels within all detected pixels.Table1describes the detection rates for the following scenes in the Rochester area.The hit rate for the RIT campus scene is78.63%, the false alarm rate is37.98%and the miss rate is21.37%.The high false alarm rate is because of the absence of the parking lanes in the truth data.In addition,many roads in the truth data do not manifest any curvilinear features or have been reallocated such as the bright road at the center of the image.However,the detector still produces relatively accurate results based on visual inspection. 4.3The Mall Scene The Mall Scene is a complex scene.Again,ground truth was obtained from the GIS database from Monroe County Government.It contains not only roads with di?erent width and orientation,but also many interference factors such as large parking lots and small road side parking lots.The road side parking lots break the curvilinearity of roads thus those parts could not be detected using the curvilinear detector.For example,an upper part of the main vertical road in Figure9a(marked in the blue box)is contiguous to a roadside parking.Furthermore the complexity of the scene leads to more false alarms.The RGB bands of the scene are shown in Figure9a and the results in Figure9. Our method successfully detected the main road even with the interference from roadside parking lots.It detected two major rail tracks that are absent from the truth data;this is due to the2-meter resolution of the image.At such resolution,the railroad track appears to be the same as regular road and it is even beyond (a)RGB image.(b)Flood?ll results.(c)Curvilinear detection results. (d)Truth data(e)Final detection Results(f)Extracted centerline Figure9:Results for the Mall scene.In(a),the red dots indicate the seeds for?ood?lling.Two rail tracks are also detected by our method due to the resolution of the image. Figure10:RGB bands of the Residential Area Scene(left),RGB bands of the trilateral?ltered image(right) with noise removed,the red dots indicate the seeds for?ood?lling. Scene Name Coverage Hit Rate Miss Rate False Alarm Rate RIT Campus1.06k m278.63%21.37%37.98% Mall2.79k m284.77%15.23%44.24% Mall(w/o rail track)87.84%12.16%34.26% Residential1.52k m298.21% 1.79%13.22% Table1:Accuracy statistics on the test scenes. the recognition of visual inspection.For a complex scene like the mall,the knowledge-based system plays an important role in re?ning the results from the curvilinear detector by merging segments logically and removing small segments.The curvilinear detector produces18475road centerline pixels.It is initially segmented to have 486parts and reduced to332grouped segments.The?nal pruned road centerline only contains54segments. 4.4The Residential Area Scene The residential area scene is a portion of a suburban area in Henrietta New York and is shown in Figure10. Ground truth is again acquired from the aforementioned GIS database.The scene contains several road types including highway entrance/exit and a residential house complex.Several factors make it a complex scene.The width of the roads changes in the scene,it contains a curved ramp at highway entrances as well as width varying exists.The road color varies from light gray to dark black and overhead trees cover portions of the roads.Many of the rooftops in the scene are built with materials spectrally similar to asphalt and this is con?rmed by the ?ood?ll map in Figure11.Furthermore,the consecutive houses often form a line that triggers false positives for the curvilinear detector. Despite the complexity of the scene,the detection still can yield high accuracy.The overall hit rate is98.21% with a miss rate of1.79%and a false alarm rate of13.22%.The system can correctly identify the highly curved highway entrance,the main road as well as the small roads in the residential complex.It is also able to avoid most of the false alarms from the house rows and overcome the partial occlusion of trees. 5.DISCUSSION In this paper,we present an integrated road extraction system.The system works on high-resolution WorldView-2images and comprises three major steps.It obtains the asphalt pixels using spatial-spectral joint?ood?ll technique and the pixels are fed into a template-matching based curvilinear detector;a knowledge-based system is then employed to segment and prune the road network.This approach works well on several scenes and has been shown to generate high accuracy;on particular scenes such as residential areas,the hit rate can reach as high as98.21%.The accuracies are summarized in Table1. A Matlab GUI was created to provide a user interface for road extraction.The main control panel consists of four sections in correspondence with steps of the algorithm.The options are stored as a structure object and (a)Flood?ll result(b)Extracted road network (c)Truth data(d)Extracted road network. Figure11:Results for the residential area scene. can be edited outside the program;a number of options can also be edited on the control panel and two windows provide previews of the scenes. The algorithm has some drawbacks at special road structures such as very short segments or circles since the curvilinear detector cannot produce su?cient responses.In several cases,the detector produces false positives on very long buildings and false negatives on roads with faint edges.However,the algorithm works well in general on diverse road structures.It has been shown to produce high accuracy results in complex and diversely structured scenes.The algorithm can overcome partial occlusions from overhead trees or cars and interference from roadside parking lots. Our paper only includes the most basic rules but the algorithm can be made more robust by adding more rules for grouping or pruning according to the scene characteristics.However one caveat is that although added rules can achieve higher accuracy on one particular scene,they may produce more unwarranted error on others. 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Construct your masterpiece - Eddie Pinero(中英文) Today belongs to the architect. He sees towers before they pierce the sky and opportunity before it transforms into the extraordinary. He knows that what he wants most can not be given. It must be sculpted. So, he builds. He builds because those skylines don't raise themselves from the earth. The extraordinary does not appear on its own. They are dreamed into existence by the architects of reality. The ones who seek not to take from the world but to contribute to it. 今天，是属于建造者的。建造者，在万丈高楼平地起之前，就看见 了根基，在奇迹出现之前，就看到了机遇。他很清楚，自己想要的 东西不是现成的，他必须下足苦功，精雕细琢。所以，他不断筑造，不断建设，因为天际线不会凭空出现，奇迹不会自己发生。是建造 者们以它们为理想，努力奋斗，才把它们变成了现实。 Today is the beginning of something new, something better, something worth remembering. And it's existed long before the present moment, waiting for your vision, your courage, your persistence to bring it to life. Too many times we fail to reach into the realm of what we know and get more of ourselves. It's okay to struggle, to be wrong, to have to rebuild a thousand times, but the great tragedy is closing our eyes and walking by life's opportunities. Everything you need to rebuild the life you dream of is around you. Your every move. If only you'd reach for it, see it before your eyes to become the architect of change. 今天是一个全新的开始，是一个更好的开始，是一个值得纪念的开始，它在很久之前就已经存在了，但你要有眼见、有胆识、有毅力，才能让它成真。很多时候，我们无法在自己擅长的领域一展身手。 挣扎，犯错，重来无数次，这都没问题。真正的悲剧在于我们对宝 贵的机会视而不见。如果你想创造自己梦寐以求的生活，其实，你 所需要的东西就在你身边，你的每个举动都与此有关。如果你能够 付出努力，预见机会，创造改变，那么，你的梦想就可以成真。 第十七课 1. agent [记忆方法] age年龄 年纪大了有事情就要找人“代理”。 agency代理处 2. ambassador [记忆方法] embassy大使馆 ambassadress大使夫人 3. annoy [记忆方法] disturb（中途打断）=interrupt annoy被打扰得恼怒 4. appeal [记忆方法] ap一再；peal隆隆之声 5. application [记忆方法] apply申请 applicant申请人 6. appoint [记忆方法] point指；ap一再 disappoint失望 7. appropriate [记忆方法] ap一再；pro一再；pri一再；ate吃 一再地把东西提前吃透了就是“合适的、适当的”。 8. approximate [记忆方法] proximate近似；ap一再 一再的近似就是“大约的”。 9. astonish [记忆方法] a一个；ston stone；ish fish 吃鱼的时候吃到一颗石头会感到“惊讶”。 10. bacteria [记忆方法] bect酒桶；teria（可以吃的）材料 酒桶中有可吃的材料时会产生“细菌”。 11. baggage [记忆方法] handbag手提袋 手提袋大到一定的标准就变成“行李”了。 12. barrier [记忆方法] bar酒吧；rier河流 河流是酒吧的“阻碍”。 barrel双筒猎枪 13. bitter [记忆方法] bit一点 14. bolt [记忆方法] 音：抱它 “螺栓”抱“螺母”。 15. brake [记忆方法] b不；rake耙子 16. breath [记忆方法] 呼吸 17. breed [记忆方法] bleed blood血 动物“繁殖”时会大量流血的。 18. brighten [记忆方法] bright明亮；en v标志 19. cable [记忆方法] ca开；able能 能够解开的绳子是“粗绳子”。 20. cassette [记忆方法] 音：开塞它 “磁带盒”是要把它打开然后把“磁带”塞进去的东西。 21. centigrade [记忆方法] cent百分之一；grade度 22. claim [记忆方法] aim目标 23. clause [记忆方法] cause原因 因为有原因才会定出一一些的“法律条款”。 24. clay [记忆方法] play玩 5．(2017·浙江卷6月)Pahlsson and her husband ________ (search) the kitchen, checking every corner, but turned up nothing. 【答案】searched 根据句意和but turned up nothing可知，search的动作发生在过去，所以填searched。 6．(2017·天津卷)I ________ (drive) down to London when I suddenly found that I was on the wrong road. 【答案】was driving 此处是be doing... when...结构，意为“正在做……，这时(突然)……”。由从句的谓语动词found可知，主句应用过去进行时。 7．(2016·浙江卷)While online shopping ________ (change) our life, not all of its effects have been positive. 【答案】has changed 主语是online shopping，结合语境以及后面的have been可知，这里要用现在完成时。 8．(2016·浙江卷)Silk ________ (become) one of the primary goods traded along the Silk Road by about 100 BC. 【答案】had become by about 100 BC是时间状语，这里强调到公元前一百年为止，所以要用过去完成时。 9．(2017·全国卷Ⅰ)When fat and salt 64. ________ (remove) from food, the food tastes as if it is missing something. 【答案】are removed 考查时态、语态和主谓一致。分析句子结构可知，64空所在的时间状语从句中没有谓语动词，故空处应填谓语动词。根据语境可知此处用一般现在时，由于主语fat and salt是复数概念，且与remove是被动关系，所以填一般现在时的被动语态结构are removed。 英语教学中的任务型语言教学初探 初次打开根据新课标要求编写的英语课本，看着这图文并茂的对话，令人耳目一新。但我们却不知道从哪儿下手，不知道该怎么备课，也不知道先讲什么，后讲什么，甚至还出现了自己不认识的单词和表达法，更不知道什么叫任务型课堂教学设计，什么样的任务才算“任务”。2005年暑假，邵武市教师进修学校的英语教研员组织全市城乡英语教师进行新一轮新课标培训，展开了学习活动，观看了课堂实录。通过参与活动，我树立了全新的课堂教学设计理念，并在教学实践中不断探索。英语课程标准明确要求，英语教学应以学生的发展为宗旨，以培养学生的创新精神和实践能力为重点；要充分发挥学生在学习过程中的主动性和积极性，激发学生的学习兴趣；营造宽松、和谐的学习氛围；使学生敢于开口，乐于实践；对学生进行思想品德教育，培养学生的思维能力，发展学生用英语获取信息、处理信息的能力；培养学生良好的英语学习心理素质和终身学习的能力。“任务型语言教学”正是以英语课程标准为原则，以人为本，培养学生的性格，发展学生的语言能力。 在使用“任务型语言教学”的过程中，我改变了以往传统的教师讲，学生记，一支粉笔，一张嘴，教师从头讲到尾教学模式。始终遵循着“任务型语言教学”的原则，即语言学习是为了用而学——在用中学——学用结合。由于任务型学习是双边或多边的交互式活动，学生的直接参与则能体现出人与人之间的交流信息、交换意见、沟通感情，建立良好的人际关系等特征。因此，在课堂活动中，我观察到学生要想完成老师设计的任务，无论是小组活动还是双人活动，他们需要张口说，需要用脑想，需要动手写或画，需要请教老师，更需要询问同伴，所有这些活动中学生总是在运用语言完成任务的过程中加深对所学语言材料的理解，而且学生根据任务需要还能产生新的语言，并与他人进行交流。 教材所设计的话题是非常贴近学生生活的，这就为他们“愿意说，有话说”奠定了良好的基础。因此，我在设计活动时，就以学生的生活经验和兴趣为出发点，内容和方式尽量真实。活动设计还尽量考虑有利于学生学习英语知识，发展语言技能，从而提高实际语言运用能力。在这半年的教学实践中，就进行课堂教学任务设计时，我尤其注重了以下几个方面： 一、任务设计的目的要明确 任务设计必须有明确的目的，要避免为了表现任务而去设计任务。在完成任务过程中，学生要能用到所学的知识和技能。设计任务一定要有助于学生理解基础知识，发展 《任务型语言教学》读后感 《任务型语言教学》读后感 读完《任务型语言教学》，心中有个最大的感受，用一个词概括就是：解惑。虽然我不敢说读完此书为心中的疑惑一扫而光（因为对任务型语言教学的研究仍在继续发展中），如果说我对任务型教学有诸多片面理解，对任务型教学只是细枝末节的把握的话，这本书使我对任务型教学有了更全面的理解，为教学实践中遇到的疑惑找到了解决的出口，为自己教学实践的改进找到了理论支撑，充实了我对英语教学改革的理解。 其一，我曾同许多同行一样片面的以为所谓任务就是在课堂上不停的进行各种活动，而回避语法知识的传授。这一度使我的教学实践出现诸多难题，也使我对任务型教学有了理解误区。其实，恰恰相反，任务型教学并不反对语法知识的传授，因为注意语法形式上任务型语言教学的主要原则之一。语言的意义与形式是学习语言的基础。任务型语言教学仍然提倡语言的学习应该是意义与形式统一。 其二，在学生没有语言能力的时候，如何能使学生完成任务。任务型学习是否反对机械性的练习。 任务型语言学习是否只是让学生在任务中自然习得语言而反对机械性的语言操练。事实上，我们在教学实践中会发现这样做很多学生无法开口，更不能“习得”。通过本书我了解我对此有多么大的误解，任务型教学不是标新立异的教学，它并不反对任务实施过程中的重复和模仿，很多研究任务型教学的学者从来都提倡教师要进行必要的语音语调的纠正，也指出了句型操练的 必要性。 其三，任务型教学中教师的课堂究竟应该怎样转变。这也是一个困扰了很久的问题，我们在完成任务的过程中往往会发现也许老师无法回答学生说提到的问题，我们已经习惯了学生按照我们所设想的答案作答。现在的问题是由于无法预知每一个学生对任务所作的结论，老师可能会碰到无法给出答案的问题。也许我们在短期内还无法适应，因为我们已经习惯自己是权威。任务型教学给了我们重新审视自己的机会。我们应该慢慢适应成为一学习的计划和组织者，尊重学生成为主体，不再习惯充当权威，成为学生学习的指导者和资源的提供者。利用所备资源引导学生进入任务，不设定任务的答案，因为它可能是开放性的。做探索知识开发学习技能 一、 Prams[n.参数] 电池组 （1）.Geometry[美[d?i'ɑ?m?tri],n.几何，几何学] 电池组 这一组都是对数据的抓取，电池都有左侧输入端和右侧输出端，都有两种输入数据的方法，一种是把相应数据连接到左侧输入端，另一种是电池上点右键 Set one XXX，新设置一个XXX。Set multipleXXX，[美['m?lt?pl],adj,多种多样的，许多的，n.倍数，关联]，即设置多个。但是Set one curve 只能选取Rhino 中创建好的，[美['ra?no?],n.犀牛] 左侧输入端：任何相应属性数据。右侧输出端：电池所包含的相应属性数据。 属性对应如下： Point:输入点数据【美[p??nt],n.点】 Vector：输入向量数据【美['vekt?r]，n，向量，矢量】 Circle：输入圆数据，这个电池只包含圆和椭圆相关曲线【美['s??rkl]】 Curve：输入曲线数据【美[k??rv]】 Plane：输入平面数据【美[ple?n]】 Circular Arc：输入圆弧数据【美['s??rkj?l?r]，adj,圆形的，循环的，美[ɑ?rk]，n,弧，弧形物】Line：输入直线数据【美[la?n]】 Rectangle：输入网格数据【美['rekt??ɡl]，n，矩形】 Box：输入实体盒子数据【美[bɑ?ks]】 Mesh:输入mesh面数据，即网格面数据【美[me?],n.网状物】 Surface:输入曲面数据，为poly曲面，不可输入mesh曲面【美['s??rf?s] n.表面，外表】 Brep:输入任意实体或者曲面数据（这个很常用）【美[b'rep]n.表面表示】 Mesh Face:与mesh类似，这里更多的是提取规则的mesh面 Twisted Box：输入北扭曲的实体【美['tw?st?d]，adj，扭曲的】 Field，输入磁场数据【美[fi?ld]】 Group：输入成组的数据【美[ɡru?p]】 Geometry：输入几何图形数据（包含点线面任何数据） Transform输入三线性集合变换图形【美[tr?ns'f??m]，v,改边，转换】 Geometry Pipeline从犀牛中输入集合管线到GH中【美['pa?pla?n]，n,管道，管线，渠道】Geometry Cache物体缓存，【美[k??]，n,隐藏所，缓存】主要作用：1、快速烘培GH汇总的物体，2、快速选择已经烘培到Rhino中的物体 （2）.Primitive 电池组【['pr?m?t?v] ，adj，原始的，简陋的】 Boolcean:输入布尔值【['bu?li?n] n,布尔布尔逻辑的】 Integer：输入整数【 ['?nt?d??r] n. [数] 整数；整体；】 Number：输入一列双精度浮点数据 Text:输入任意文字 Color：输入一列颜色参数的RGB值【['k?l?r]】 Culture：包含了一系列文化特征【[?k?lt??] n.文化，修养】 Domain2:输入任意二维区间数据或者UV范围【[do?'me?n]N. 领地；领域；范围】 Matrix:包含了一系列的数据矩阵【['me?tr?ks] n 矩阵】 Complex:代表一个复核的集合。复杂的参数能够存储持久数据。你可以通过参数设置菜单的持续记录。【[k?m'pleks] adj.复杂的；合成的；复合的】 Domain:输入任意二维区间数据 Guide:输入任意一个参量的编号代码，方便其他电池找到此参量【[ɡa?d] n.指南；向导；入门书】Time:输入时间和日期数据 Date：输入任何一列参量 File Path:用于输入硬盘中某个地址的文件【[fa?l] n. 文件；[p?θ] n. 道路；小路；】 Date Path：通过路径输入一列数据【[det] n. 日期；约会；】 construct的同义词和例句 construct同义词： build, construct, found, erect, establish, set up construct同义词辨析： 这些动词均有"建设，建立，建造"之意。 build 普通用词，含义广泛，可指一切具体或抽象的建造或建立。 construct 较正式用词，强调根据一定计划进行的规模较大，结构较复杂，要求较高技术的建造。 found 侧重打下基础或创办，具体或抽象事物均可用。 erect 侧重指对高而垂直物的建造。使用不如build广泛。 establish 着重稳固地建成，可具体指国家、政府、学校或商店等的建立，也可指信仰、信用、名誉、法律、制度、规则等的建立。 set up 作"建立"用时，侧重于"开始"。可指具体或抽象的建立。 construct的例句： 1. any attempt to construct an ideal society is foredoomed to failure. 任何构建理想社会的努力都注定要失败。 2. it is proverbially easier to destroy than to construct. 谁都知道破坏容易建设难. 3. how many man - days will be needed to construct this irrigation canal? 修这条渠道要多少人工? 4. every citizen has the duty to construct his country. 每个公民都有建设祖国的责任. 5. let's construct a square on this line. 让我们以这条线做一个正方形. 6. use your protractor to construct an equilateral triangle. 用量角器作一个等边三角形. 7. now they can construct tunnel systems without hindrance. 现在他们可以顺利地建造隧道系统了. 8. you will find it difficult to construct a spending plan without first recording your spending. 你会发现不先把自己的开销记录下来就很难制订支出计划。 9. it was a re-enactment of the same mental construct under which slavery was justified. 这是为奴隶制辩护的同一思想观念的再现。 10. the country was an artificial construct held together by force and intimidation for more than 70 years. 这个国家是靠武力和恐吓维系达七十余年的人治政权。 11. among them , the tibet museum cost nearly 100 million 任务型语言教学 任务是与目标密切相关的活动，既可以是个人活动，也可以是集体活动。香港中小学英语大纲把任务定义为：给学生提供机会使他们能够体验、发现和运用口头和书面语言的、有实际目的的各种教学活动。例如，让初一学生做类似于“What’s your name？”“How old are you？”的对话练习，严格意义上讲，这不是一个任务，因为学生并不关心对方所说的内容（由于是同班同学，这些信息早已熟悉）。这些角色扮演的活动并非我们所说的任务型语言教学中的任务，因为它把注意力集中在形式上，而不是语言的意义上。但是，如果让小学生在名片上写名字，记录下真实的名字等信息，就成为了一个任务。用通俗的话讲，任务是人们在日常生活中所从事的有目的的活动。例如：粉刷栅栏、给孩子穿衣服、填表格、买双鞋、从图书馆借本书、开车路考、预定机票、写支票、寻找街上某个目的地等，这些都是各种各样的任务。我们的任务型语言教学也就是根据人们实际生活使用语言的情况去设计各种各样的学习任务，模拟使用语言的情况。在生活中使用语言，这便是任务型语言教学的核心思想。纽南（1989）说：“交际型语言学习任务是一种涉及到学习者理解、运用所学进行交流的课堂活动。学生的注意力主要集中在语言的意义上，而不是语言的形式上。”语言学习的重点应该是理解和使用语言，而不只是注重语言的形式。理解本身也是一个任务。例如，一个教师讲数字时，让学生把电话号码及车牌号写下来做成电话簿并根据教师读的数字领取自己的自行车钥匙，这样，听的练习也就成了一个任务，也是一个理 解的过程。学生注意力集中，理解并创造语言，而且这样的任务有看得见、摸得着的结果，即做了一个电话簿，拿回了自己的车钥匙，这是一个非语言的结果（non-verbal product）。再比如，学生听天气预报然后决定到某地的穿着（非语言结果）、看图然后决定做什么事、写回复（语言结果）、描述家庭照片、完成银行申请等。任务可以有很多起点，学习者向外界（如他人）提供自己的个人信息或发表个人观点和看法、做口头信息的记录、表达情感等，这些都可以是任务的起始点。 以上是关于任务型语言教学的总的认识。但是，由于不同学者和研究者的研究背景的差异，以及实际教学中的各种偏差，人们对任务的理解则不尽相同。 首先，关于任务的定义，有狭义任务派和广义任务派。狭义任务派认为，课堂教学中的很多活动并不是任务，而只是设定好让学生做某种机械联系的操练活动中，这本身不能称之为任务。威利斯（1996）说：“在很多角色的扮演活动中，学生并需要获得任何实际的结果，他们只是扮演不同的角色而已。虽然学生也得想一想说哪些事情，但他们不太可能真正的交换意义。”所以，不涉及到真正意义交流的活动，不能称为任务。一下活动可以称为任务：听天气预报决定穿什么。这个任务的结果是，学习者听天气预报后辨认出当天的最高温度。但是，如果练习的结果是学习者听课文录音之后回答问题，或者判断给出的陈述是否真确，那么这个活动就不是任务了。狭义任务派认为任务与语言练习有着本质的区别。任务活动所谋求的效果不是一种机械 在新目标英语《Go for it》教学中，主要采用任务型语言教学（Task-Bas ed Language Teaching）模式，融会话题、交际功能和语言结构，形成了一套循序渐进的生活化的学习体系。《英语课程标准》对任务是这样描述的：“激发和培养学生学习英语的兴趣，使学生树立自信心，使学生掌握一定的英语基础知识和听、说、读、写技能，形成一定的综合语言运用能力；培养学生的观察、记忆、思维、想象能力和创新精神”。 一、采用“任务型”教学途径，教师应懂得如何设计任务。在设计任务时，教师应根据学生的学习兴趣和语言水平，找出教学内容与学习生活经验和兴趣之间的最佳结合点，恰当地确定任务的内容和形式。 1、任务的设计要有兴趣性、真实性任务型教学活动是以学生的兴趣和生活经验为出发点，内容和方式真实，旨在培养学生在真实生活中运用语言的能力，让学生在教学活动中参与和完成任务，摆脱传统的以教材知识为中心的枯燥无味的教学方式，使课堂语言活动近似于习得过程。在《Go For It》中得到充分体现。如，七年级下册Unit 1 Where is you pen pal from?一课，教学目的是让学生掌握一些国名，民族及语言。根据学生的兴趣和年龄特征，教师课前让学生收集自己喜爱的球星、歌星，一些城市景点等相关资料。上课时教师在黑板上贴一幅世界地图，先将学生分成4—6人一组，让他们自己在组内应用手中的资料交流，然后推荐2人到讲台前介绍自己喜爱的人物，介绍完后再将他们的照片贴在地图上相应的国家上面。在这一任务设计中，通过听、说、演、练等活动，把抽象的语言文字和有趣的实际生活应用联系起来，使学生在完成任务的过程中体验到成功。在这样的氛围中学生愿学、乐学，有很高的学习积极性。 2 、任务的设计要有层次性任务越容易，参加任务的人就越多，获得的成功体验也就越多，学习兴趣就会越浓厚。因此，在设计任务时应循序渐进，由易到难。刚开始设计的任务应稍微简单些，让大部分学生都容易参与进来。随着任务的渐渐深入，学生对任务信息的了解不断增加，稍难的任务也会显得容易 construction在合同中的2层常见含义 construction，最常见的含义是“建设”，与合同相关的，比如：construction contract建设工程合同；construction interest建设利息等等，但是它还有另外一层含义，那就是“解释”，比如：construction of the contract in accordance with the law（根据法律规定解释合同）；put a construction on（对......作出解释）： （1）建设，是construct的名词形式； （2）解释，是construe的名词形式。 1 From now on,our major projects will be redirected to the housing construction. 今后我们的重点工程将转到住宅建设上去。 2 例句一： Without limiting the generality of the foregoing, the doctrine of contra proferentem shall not have any application to the construction of this Agreement. 在不限制前述一般性的前提下，《解释原则》对本协议的解释应一概不适用。 例句二： 本协议受中华人民共和国法律管辖，并在所有方面依其进行解释。 This Agreement is governed by and in all respects construed in accordance with the laws of the People’s Republic of China. 以上仅代表旗渡观点，欢迎批评指正讨论交流。 Engineer，Procure，Construct(EPC)模式EPC合同条件的基本出发点是业主参与工程管理工作很少，因承包商已承担了工程建设的大部分风险，业主重点进行竣工验收。但是，如果委派业主代表则不同，在有的实际工程中，业主委派某个工程项目管理公司作为其代表，从而对建设工程的实施从设计、采购到施工进行全面的严格管理。 EPC模式的固定总价合同 EPC合同更接近于固定总价合同。在国际工程承包中，固定总价合同仅用于规模小、工期短的工程。而EPC模式所适用的工程一般规模均较大，工期较长，且具有相当的技术复杂性。在EPC模式条件下，业主允许承包商因费用变化而调价的情况是不多见的。 EPC模式适用于工程所包含的地下隐蔽工作不多，承包商在投标前无法进行勘察的工作区域也不能太大，否则，承包商就无法判定具体的工程量，增加了承包商的风险。 既然合同规定由承包商负责全部设计，并承担工程全部责任，故业主不能过多地干预承包商的工作。要求所完成的工程符合“合同中预期的工程之目的”就应认为承包商履行了合同中的义务。优选承包商时应突出对承包商过去业绩的审查，对投标书中技术文件的审查以及质量管理体系的审查。 由于采用总价合同，故期中支付可以按月度支付，也可以按阶段支付，在合同中可以规定每次支付款的具体数额或合同价的百分率。 工程项目管理是按客观经济规律对工程项目建设全过程进行有效的计划、组织、控制、协调的系统管理活动。从内容上看，它是工程项目建设全过程的管理，即从项目建议书、可行性研究分析、工程设计、工程施工到竣工投产全过程的管理。从性质上看，项目管理是固定资产投资管理的微观基础，其性质 属投资管理范畴。工程项目管理模式，是指将管理的对象作为一个系统，通过一定的组织和管理方式，使系统能够正常运行，并确保其目标的实现。 近年来，一些国际上比较先进的工程公司如BECHTEL、FLOUR、FOSTER、WHEELER、KBR、AMEC、JGC、LUMMUS等为了适应项目建设大型化、一体化以及项目大规模融资和分散项目风险的需要，推出了一些成熟的项目管理方式。 传统的项目管理模式（DBB模式） 即设计-招标-建造（Design-Bid-Build）模式。该管理模式在国际上为通用，世行、亚行贷款项目及以国际咨询工程师联合会（FIDIC）合同条件为依据的项目均采用这种模式。突出的特点是强调工程项目的实施必须按照设计-招标-建造的顺序方式进行，只有一个阶段结束后另一个阶段才能开始。在DBB模式中，参与项目的主要三方是业主、建筑师/工程师、承包商。 优点：通用性强；可自由选择咨询、设计、监理方；各方均熟悉使用标准的合同文本，有利于合同管理、风险管理和减少投资。 缺点：工程项目要经过规划、设计、施工三个环节之后才移交给业主，项目周期长；业主管理费用较高，前期投入大；变更时容易引起较多索赔。 建筑工程管理方式（CM模式） 又称阶段发包方式，业主在项目开始阶段就雇用施工经验丰富的咨询人员即CM经理，参与到项目中来，负责对设计和施工整个过程的管理。它打破过去那种待设计图纸完成后，才进行招标建设的连续建设生产方式。其特点是：由业主和业主委托的工程项目经理与工程师组成一个联合小组共同负责组织和管理工程的规划、设计和施工。完成一部分分项（单项）工程设计后，即对该 程晓堂《任务型语言教学》笔记 第一章任务型语言教学概论 第一节什么是任务性语言教学 英文Task-based Language Teaching (TBLT) 定义：是一种基于任务或以任务为基础的语言教学途径（也有人称之为任务型教学法）。根据这一教学途径，设计语言教学大纲以及编写语言教材不是按一定的顺序罗列或介绍语言项目（Linguistics items）,而是设计一系列任务。在具体教学过程中，学生不是逐一学习各个项目语言，而是完成各种各样的交际任务。 解析：1 要求学生在进行近似日常生活中和工作中我们所做的各种各样的事情的过程中，学习语言和使用语言，所谓“在做中学，在用中学”。 2 与情景法比较情景法是对已学内容的模仿演练（注重表达法），缺乏真实性，学生没有自我发挥空间。任务型，以完成某个任务为目的，把对话当做过程和手段（注重相互间传达的意义），学生所使用的语言不受限，不过分关注语言运用的准确性和正确性。 3 是一种教学途径，而不是具体教学方法。在任务型语言教学思路指导下，可以设计各种各样具体的教学活动或任务。 第二节任务型语言教学从何而来 一交际语言教学的进一步发展 交际语言的核心思想：表达意义（express meaning），传递信息(convey information ) 优点：a要表达要传递的目的激发学习动机，调动学习兴趣 b 提供交际情境，有利于形成交际能力和运用语言能力 要求：真实（贴近现实）互动的活动（学习者之间，学习者和老师） 发展：交际语言没落没落后，这方面的专家把重心转向任务，进行进一步研究。 二第二语言习得研究的结果 Krashen的输入理论 要成功习得语言，就要有足够的可理解的语言输入→有足够输入外，还要有输出的过程（Swain）→任务能够给学习者提供输入和输出的机会（Richards & Rodgers）→意义协商是二语习得能否取得成功的关键（Long）→互动理论（the Interaction Hypothesis）→开始关注任务→把任务与教学实践连接起来 意义协商（meaning negotiation）：在语言习得过程中，学习这不是简单的接受语言，然后输出语言，而是在不间断的输入——反馈——调整——再输入——输入的过程中完成意义的接收和表达。 互动理论：核心思想是意义协商的过程是促进语言学习的关机因素。 三两个来源之间的关系 共同认识：语言学习的目的不是掌握语言的结构，而是发展表达意义的能力。而且这种能力不可能通过以语法为中心的学习活动来实现，只能通过交际性活动（communication activity）来培养。重点在研究交际性语言学习活动。 不同点（研究角度）：语言教学法研究者从课堂教学需要的出发，研究交际性活动的设计与使用。第二语言习得的研究者从认知的角度研究交际活动（即任务），在学习者中介语的过程中的作用。 第三节任务性语言教学的理论基础 一任务型教学的语言观 语言能力：a 有关语篇结构(discourse stractures )的知识以及合理运用语篇结构的能力 b 根据语境调整词汇，语篇结构的能力 雅思写作大作文范文雅思写作讨论双方观点建造新铁路construct new railways 今天我们雅思写作大作文范文的文章来探讨下是否应该建造新的铁路。不可否认，铁路的建设为居民出行提供了很大的便利。身为中国人，我们对这一点的感触尤其深。高铁将过去一整天的路程缩短到几个小时。但这是否意味着国家应该将更多的预算投入新铁路的修建中，而非提升现有的道路交通网络呢？小编搜集了一篇相关的高分范文，以供大家参考。 雅思写作大作文题目 In a number of countries, some people think it is necessary to spend large sums of money on constructing new railway lines for very fast trains between cities. Others believe the money should be spent on improving existing public transport. Discuss both these views and give your own opinion. 在许多国家，一些人认为有必要花很多钱在城市之间为快速列车修建新的铁路。其他人则认为金钱应该用在提升现有的公共交通上面。讨论双方观点，并给出你自己的意见。 雅思写作大作文范文 People, in some countries, prefer to have a fast and extensive rail communication while others think that improving the existing road transportation system is far more important and that is why more budget should be spent on roads. This essay discusses both views and includes my opinion before drawing a reasonable conclusion. 一些国家的人们更加喜欢拥有快速而广阔的铁道交通网络。而其他人则认为提升现有的道路交通系统要重要的多。这也是为什么更多的预算应该花在公路上的原因。这篇文章讨论了两种观点，并且在得出结论之前也包括了我自己的观点。 On the one hand, people in favor of having a speedy and reliable train communication system mainly base their ground on the fact that trains can carry a great number of passengers and goods at a time and can remarkably reduce the traffic congestion. Rail communication is safer, faster and can connect cities and towns that build a better communication system than the conventional road transport system, according to these people. Furthermore, locomotives are already famous in many countries and have greatly improved communications there. Due to such advantages, people often opine that more money should be allocated for constructing new train lines. This article is from Laokaoya website. For instance, the train system in China has a groundbreaking contribution to improving its overall communication system and many countries have started adopting this model. 一方面，支持快速可靠铁路交通系统的人主要的依据是火车可以一次承载大量的乘客和物品，并显著的减少交通拥堵。根据这些人的观点，轨道交通更加安全、快速并且可以连接城市和乡镇，从而建立起比传统道路交通系统更加良好的交流网络。此外，火车在许多国家已经十分出名，比ing切已经极大地提升了地区之间的交流。由于这些好处，人们通常认为 1任务的概念 2任务与练习的区别 3任务的构成要素 4任务型教学的概念 5任务型教学的特点 6任务设计的原则 7在任务型英语课堂教学模式中，任务的分配方式 8任务型教学模式尚非完美，在实施过程中遭遇到一些困难学生教师 9在设计“任务”时，教师注意的问题： Mr. Long (1985)显然这是对任务最原始的定义，这类任务不含学术成分，也无语言意义，与教学多大关系。 Richards, Platt and Weber(1986) .即“任务”是学生为达到某一交际目的，遵循意义优先，任务完成、优先的原则。 Richards, Platt and Weber(1986) ：课堂开展什么样的活动，活动的内涵等阐述不够，因而对于课堂实践操作缺少指导意义。 明确了任务活动并非练习活动，完成任务的过程实质是交际的过程不必过分重视语言形式。 David Nunan(1989): 交际任务是指导学生在学习目标语的过程中领悟、使用、产出和互动的课堂交际活动，它关注的是语言意义、而不是语言形式，它完全有别于语法练习。这是对“任务”最科学、最权威，影响力最大的定义，David Nunan 全面吸收了众多语言学家的研究成果，认为任务由教学目标、输入、活动形式、教师与学生的角色和环境五部分组成。这是我国任务型英语教学最全面科学的理论指导。 任务型教学的特点 （一）目标如同日常生活和工作中的任务一样，教学任务首先具有目的性，也就是说，它应该具有较为明确的目标指向。如前所述，这种目标指向具有两重性，一是任务本身要达到的非教学目的，二是利用任务所要达到的预期的教学目的。如在“案件侦破”任务中，其非教学目的便是根据不断增加的线索进行讨论推理，直到最后找出罪犯。但设计任务所期望达到的教学目标则可能是通过完成任务过程中所产生的语言交流感受语言，增强语言意识，提高交际能力，并在交际过程中应用诸如表示假设、因果关系，或“肯定”“可能”“也许”等目的语表达形式。作为促进学习的教学任务，教师更多地关注它的教学目的。 （二）内容任务的这一要素可简单地表达为“做什么”。任何一个任务都需赋予它实质性的内容，任务的内容在课堂上的表现就是需要履行的具体的行为和活动。 （三）程序指学习者在履行某一任务过程中所涉及的操作方法和步骤，在一定程度上表现为“怎样做”。它包括任务序列中某一任务所处的位置、先后次序、时间分配等。 （四）输入材料所谓输入材料是指履行任务过程中所使用或依据的辅助资料。输入材料可以是语言的，如新闻报道、旅游指南、产品使用说明、天气预报等；也可以是非语言的，如一叠照片、图表、漫画、交通地图、列车时刻表等。尽管有些课堂任务并不一定都要使用或 construct的用法和短语例句 construct有建造;构筑;造(句)等意思，那么你知道construct的用法吗?下面是小编为大家整理的construct的用法和相关短语例句，欢迎大家学习! construct的用法： construct的用法1：construct的基本意思是修造建造,指把建筑材料组合在一起建成某物。本词不强调花费的劳动,尤其不强调体力劳动,而强调动脑解决如何建造的问题,即构思过程。 construct的用法2：construct是及物动词,接名词或代词作宾语,可用于被动结构。 construct的常用短语： 用作动词(v.) construct from (v.+prep.) construct (out) of (v.+prep.) construct的用法例句： 1. Any attempt to construct an ideal society is foredoomed to failure. 任何构建理想社会的努力都注定要失败。 2. It is proverbially easier to destroy than to construct. 谁都知道破坏容易建设难. 3. How many man - days will be needed to construct this irrigation canal? 修这条渠道要多少人工? 4. Every citizen has the duty to construct his country. 每个公民都有建设祖国的责任. 5. Lets construct a square on this line. 让我们以这条线做一个正方形. 6. Use your protractor to construct an equilateral triangle. 用量角器作一个等边三角形. 7. Now they can construct tunnel systems without hindrance. 现在他们可以顺利地建造隧道系统了. 8. You will find it difficult to construct a spending plan without first recording your spending. 你会发现不先把自己的开销记录下来就很难制订支出计划。 9. It was a re-enactment of the same mental construct under which slavery was justified. 这是为奴隶制辩护的同一思想观念的再现。 10. The country was an artificial construct held together by force and intimidation for more than 70 years. 这个国家是靠武力和恐吓维系达七十余年的人治政权。 11. Among them , the Tibet Museum cost nearly 100Construct your masterpiece - Eddie Pinero(中英文)
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